To see the other types of publications on this topic, follow the link: Sequential rules and patterns.
Journal articles on the topic 'Sequential rules and patterns'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 journal articles for your research on the topic 'Sequential rules and patterns.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.
1
Chen, Yen-Liang, Shih-Sheng Chen, and Ping-Yu Hsu. "Mining hybrid sequential patterns and sequential rules." Information Systems 27, no. 5 (July 2002): 345–62. http://dx.doi.org/10.1016/s0306-4379(02)00008-x.
Full text
2
Tsai, Chieh-Yuan, and Sheng-Hsiang Huang. "Integrating Product Association Rules and Customer Moving Sequential Patterns for Product-to-Shelf Optimization." International Journal of Machine Learning and Computing 5, no. 5 (October 2015): 344–52. http://dx.doi.org/10.7763/ijmlc.2015.v5.532.
Full text
3
Zhou, Shenghan, Houxiang Liu, Bang Chen, Wenkui Hou, Xinpeng Ji, Yue Zhang, Wenbing Chang, and Yiyong Xiao. "Status Set Sequential Pattern Mining Considering Time Windows and Periodic Analysis of Patterns." Entropy 23, no. 6 (June 11, 2021): 738. http://dx.doi.org/10.3390/e23060738.
Full textAbstract:
The traditional sequential pattern mining method is carried out considering the whole time period and often ignores the sequential patterns that only occur in local time windows, as well as possible periodicity. Therefore, in order to overcome the limitations of traditional methods, this paper proposes status set sequential pattern mining with time windows (SSPMTW). In contrast to traditional methods, the item status is considered, and time windows, minimum confidence, minimum coverage, minimum factor set ratios and other constraints are added to mine more valuable rules in local time windows. The periodicity of these rules is also analyzed. According to the proposed method, this paper improves the Apriori algorithm, proposes the TW-Apriori algorithm, and explains the basic idea of the algorithm. Then, the feasibility, validity and efficiency of the proposed method and algorithm are verified by small-scale and large-scale examples. In a large-scale numerical example solution, the influence of various constraints on the mining results is analyzed. Finally, the solution results of SSPM and SSPMTW are compared and analyzed, and it is suggested that SSPMTW can excavate the laws existing in local time windows and analyze the periodicity of the laws, which solves the problem of SSPM ignoring the laws existing in local time windows and overcomes the limitations of traditional sequential pattern mining algorithms. In addition, the rules mined by SSPMTW reduce the entropy of the system.
4
Ramadhan, Dayan Ramly, and Nur Rokhman. "Segmentation-Based Sequential Rules For Product Promotion Recommendations As Sales Strategy (Case Study: Dayra Store)." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 14, no. 3 (July 31, 2020): 243. http://dx.doi.org/10.22146/ijccs.58107.
Full textAbstract:
One of the problems in the promotion is the high cost. Identifying the customer segments that have made transactions, sellers can promote better products to potential consumers. The segmentation of potential consumers can be integrated with the products that consumers tend to buy. The relationship can be found using pattern analysis using the Association Rule Mining (ARM) method. ARM will generate rule patterns from the old transaction data, and the rules can be used for recommendations. This study uses a segmented-based sequential rule method that generates sequential rules from each customer segment to become product promotion for potential consumers. The method was tested by comparing product promotions based on rules and product promotions without based on rules. Based on the test results, the average percentage of transaction from product promotion based on rules is 2,622%, higher than the promotion with the latest products with an average rate of transactions only 0,315%. The hypothesis in each segment obtained from the sample can support the statement that product promotion in all segments based on rules can be more effective in increasing sales compared to promotions that use the latest products without using rules recommendations.
5
Yasmin, Regina Yulia, Putri Saptawati, and Benhard Sitohang. "Classification with Single Constraint Progressive Mining of Sequential Patterns." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 4 (August 1, 2017): 2142. http://dx.doi.org/10.11591/ijece.v7i4.pp2142-2151.
Full textAbstract:
<span>Classification based on sequential pattern data has become an important topic to explore. One of research has been carried was the Classify-By-Sequence, CBS. CBS classified data based on sequential patterns obtained from AprioriLike sequential pattern mining. Sequential patterns obtained were called CSP, Classifiable Sequential Patterns. CSP was used as classifier rules or features for the classification task. CBS used AprioriLike algorithm to search for sequential patterns. However, AprioriLike algorithm took a long time to search for them. Moreover, not all sequential patterns were important for the user. In order to get the right and meaningful features for classification, user uses a constraint in sequential pattern mining. Constraint is also expected to reduce the number of sequential patterns that are short and less meaningful to the user. Therefore, we developed CBS_CLASS* with Single Constraint Progressive Mining of Sequential Patterns or Single Constraint PISA or PISA*. CBS_Class* with PISA* was proven to classify data in faster time since it only processed lesser number of sequential patterns but still conform to user’s need. The experiment result showed that compared to CBS_CLASS, CBS_Class* reduced the classification execution time by 89.8%. Moreover, the accuracy of the classification process can still be maintained.</span><p> </p>
6
Rana, Toqir A., and Yu-N. Cheah. "Sequential Patterns-Based Rules for Aspect-Based Sentiment Analysis." Advanced Science Letters 24, no. 2 (February 1, 2018): 1370–74. http://dx.doi.org/10.1166/asl.2018.10752.
Full text
7
HUANG, XIANGJI. "COMPARISON OF INTERESTINGNESS MEASURES FOR WEB USAGE MINING: AN EMPIRICAL STUDY." International Journal of Information Technology & Decision Making 06, no. 01 (March 2007): 15–41. http://dx.doi.org/10.1142/s0219622007002368.
Full textAbstract:
A common problem in mining association rules or sequential patterns is that a large number of rules or patterns can be generated from a database, making it impossible for a human analyst to digest the results. Solutions to the problem include, among others, using interestingness measures to identify interesting rules or patterns and pruning rules that are considered redundant. Various interestingness measures have been proposed, but little work has been reported on the effectiveness of the measures on real-world applications. We present an application of Web usage mining to a large collection of Livelink log data. Livelink is a web-based product of Open Text Corporation, which provides automatic management and retrieval of different types of information objects over an intranet, an extranet or the Internet. We report our experience in preprocessing raw log data, mining association rules and sequential patterns from the log data, and identifying interesting rules and patterns by use of interestingness measures and some pruning methods. In particular, we evaluate a number of interestingness measures in terms of their effectiveness in finding interesting association rules and sequential patterns. Our results show that some measures are much more effective than others.
8
Gong, Yongshun, Tiantian Xu, Xiangjun Dong, and Guohua Lv. "e-NSPFI: Efficient Mining Negative Sequential Pattern from Both Frequent and Infrequent Positive Sequential Patterns." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 02 (January 12, 2017): 1750002. http://dx.doi.org/10.1142/s0218001417500021.
Full textAbstract:
Negative sequential patterns (NSPs), which focus on nonoccurring but interesting behaviors (e.g. missing consumption records), provide a special perspective of analyzing sequential patterns. So far, very few methods have been proposed to solve for NSP mining problem, and these methods only mine NSP from positive sequential patterns (PSPs). However, as many useful negative association rules are mined from infrequent itemsets, many meaningful NSPs can also be found from infrequent positive sequences (IPSs). The challenge of mining NSP from IPS is how to constrain which IPS could be available used during NSP process because, if without constraints, the number of IPS would be too large to be handled. So in this study, we first propose a strategy to constrain which IPS could be available and utilized for mining NSP. Then we give a storage optimization method to hold this IPS information. Finally, an efficient algorithm called Efficient mining Negative Sequential Pattern from both Frequent and Infrequent positive sequential patterns (e-NSPFI) is proposed for mining NSP. The experimental results show that e-NSPFI can efficiently find much more interesting negative patterns than e-NSP.
9
Gillett, Maxwell, Ulises Pereira, and Nicolas Brunel. "Characteristics of sequential activity in networks with temporally asymmetric Hebbian learning." Proceedings of the National Academy of Sciences 117, no. 47 (November 11, 2020): 29948–58. http://dx.doi.org/10.1073/pnas.1918674117.
Full textAbstract:
Sequential activity has been observed in multiple neuronal circuits across species, neural structures, and behaviors. It has been hypothesized that sequences could arise from learning processes. However, it is still unclear whether biologically plausible synaptic plasticity rules can organize neuronal activity to form sequences whose statistics match experimental observations. Here, we investigate temporally asymmetric Hebbian rules in sparsely connected recurrent rate networks and develop a theory of the transient sequential activity observed after learning. These rules transform a sequence of random input patterns into synaptic weight updates. After learning, recalled sequential activity is reflected in the transient correlation of network activity with each of the stored input patterns. Using mean-field theory, we derive a low-dimensional description of the network dynamics and compute the storage capacity of these networks. Multiple temporal characteristics of the recalled sequential activity are consistent with experimental observations. We find that the degree of sparseness of the recalled sequences can be controlled by nonlinearities in the learning rule. Furthermore, sequences maintain robust decoding, but display highly labile dynamics, when synaptic connectivity is continuously modified due to noise or storage of other patterns, similar to recent observations in hippocampus and parietal cortex. Finally, we demonstrate that our results also hold in recurrent networks of spiking neurons with separate excitatory and inhibitory populations.
10
Höpken, Wolfram, Marcel Müller, Matthias Fuchs, and Maria Lexhagen. "Flickr data for analysing tourists’ spatial behaviour and movement patterns." Journal of Hospitality and Tourism Technology 11, no. 1 (February 26, 2020): 69–82. http://dx.doi.org/10.1108/jhtt-08-2017-0059.
Full textAbstract:
Purpose The purpose of this study is to analyse the suitability of photo-sharing platforms, such as Flickr, to extract relevant knowledge on tourists’ spatial movement and point of interest (POI) visitation behaviour and compare the most prominent clustering approaches to identify POIs in various application scenarios. Design/methodology/approach The study, first, extracts photo metadata from Flickr, such as upload time, location and user. Then, photo uploads are assigned to latent POIs by density-based spatial clustering of applications with noise (DBSCAN) and k-means clustering algorithms. Finally, association rule analysis (FP-growth algorithm) and sequential pattern mining (generalised sequential pattern algorithm) are used to identify tourists’ behavioural patterns. Findings The approach has been demonstrated for the city of Munich, extracting 13,545 photos for the year 2015. POIs, identified by DBSCAN and k-means clustering, could be meaningfully assigned to well-known POIs. By doing so, both techniques show specific advantages for different usage scenarios. Association rule analysis revealed strong rules (support: 1.0-4.6 per cent; lift: 1.4-32.1 per cent), and sequential pattern mining identified relevant frequent visitation sequences (support: 0.6-1.7 per cent). Research limitations/implications As a theoretic contribution, this study comparatively analyses the suitability of different clustering techniques to appropriately identify POIs based on photo upload data as an input to association rule analysis and sequential pattern mining as an alternative but also complementary techniques to analyse tourists’ spatial behaviour. Practical implications From a practical perspective, the study highlights that big data sources, such as Flickr, show the potential to effectively substitute traditional data sources for analysing tourists’ spatial behaviour and movement patterns within a destination. Especially, the approach offers the advantage of being fully automatic and executable in a real-time environment. Originality/value The study presents an approach to identify POIs by clustering photo uploads on social media platforms and to analyse tourists’ spatial behaviour by association rule analysis and sequential pattern mining. The study gains novel insights into the suitability of different clustering techniques to identify POIs in different application scenarios.
11
Zhang, Kechen. "How to Compress Sequential Memory Patterns into Periodic Oscillations: General Reduction Rules." Neural Computation 26, no. 8 (August 2014): 1542–99. http://dx.doi.org/10.1162/neco_a_00618.
Full textAbstract:
A neural network with symmetric reciprocal connections always admits a Lyapunov function, whose minima correspond to the memory states stored in the network. Networks with suitable asymmetric connections can store and retrieve a sequence of memory patterns, but the dynamics of these networks cannot be characterized as readily as that of the symmetric networks due to the lack of established general methods. Here, a reduction method is developed for a class of asymmetric attractor networks that store sequences of activity patterns as associative memories, as in a Hopfield network. The method projects the original activity pattern of the network to a low-dimensional space such that sequential memory retrievals in the original network correspond to periodic oscillations in the reduced system. The reduced system is self-contained and provides quantitative information about the stability and speed of sequential memory retrievals in the original network. The time evolution of the overlaps between the network state and the stored memory patterns can also be determined from extended reduced systems. The reduction procedure can be summarized by a few reduction rules, which are applied to several network models, including coupled networks and networks with time-delayed connections, and the analytical solutions of the reduced systems are confirmed by numerical simulations of the original networks. Finally, a local learning rule that provides an approximation to the connection weights involving the pseudoinverse is also presented.
12
Rana, Toqir A., and Yu-N. Cheah. "Sequential patterns rule-based approach for opinion target extraction from customer reviews." Journal of Information Science 45, no. 5 (October 23, 2018): 643–55. http://dx.doi.org/10.1177/0165551518808195.
Full textAbstract:
Aspect extraction or opinion target extraction is the key task of sentiment analysis, which aims to identify targets of people’s sentiments. This is the most important task of aspect-based sentiment analysis as without the aspects, there is no much use of extracted opinions. Recent approaches have shown the significance of dependency-based rules for the given task. These rules are heavily dependent on the dependency parser and generated with the help of grammatical rules. In this article, we are proposing to learn from user’s behaviour to identify the relation among aspects and opinions. The use of sequential patterns has been proposed for the extraction of aspects. The key purpose of this research is to study the impact of sequential pattern mining in the phase of aspect extraction. Our experimental results show that the approach proposed in our work produced better results as compared with the state-of-the-art approaches.
13
Akhondzadeh-Noughabi, Elham, and Amir Albadvi. "Mining the dominant patterns of customer shifts between segments by using top-k and distinguishing sequential rules." Management Decision 53, no. 9 (October 19, 2015): 1976–2003. http://dx.doi.org/10.1108/md-09-2014-0551.
Full textAbstract:
Purpose – The purpose of this paper is to detect different behavioral groups and the dominant patterns of customer shifts between segments of different values over time. Design/methodology/approach – A new hybrid methodology is presented based on clustering techniques and mining top-k and distinguishing sequential rules. This methodology is implemented on the data of 14,772 subscribers of a mobile phone operator in Tehran, the capital of Iran. The main data include the call detail records and event detail records data that was acquired from the IT department of this operator. Findings – Seven different behavioral groups of customer shifts were identified. These groups and the corresponding top-k rules represent the dominant patterns of customer behavior. The results also explain the relation of customer switching behavior and segment instability, which is an open problem. Practical implications – The findings can be helpful to improve marketing strategies and decision making and for prediction purposes. The obtained rules are relatively easy to interpret and use; this can strengthen the practicality of results. Originality/value – A new hybrid methodology is proposed that systematically extracts the dominant patterns of customer shifts. This paper also offers a new definition and framework for discovering distinguishing sequential rules. Comparing with Markov chain models, this study captures the customer switching behavior in different levels of value through interpretable sequential rules. This is the first study that uses sequential and distinguishing rules in this domain.
14
Han, Hong Kyu, Hong Sik Kim, and So Young Sohn. "Sequential association rules for forecasting failure patterns of aircrafts in Korean airforce." Expert Systems with Applications 36, no. 2 (March 2009): 1129–33. http://dx.doi.org/10.1016/j.eswa.2007.10.012.
Full text
15
Saputra, Diki Andika, Eneng Tita Tosida, and Fajar Delli W. "PENENTUAN POLA SEKUENSIAL DATA TRANSAKSI PENJUALAN MENGGUNAKAN ALGORITMA SEQUENTIAL PATTREN DISCOVERY USING EQUIVALENT CLASSES (SPADE)." Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika 16, no. 2 (December 31, 2019): 271–82. http://dx.doi.org/10.33751/komputasi.v16i2.1621.
Full textAbstract:
Transaction data is customer or customer data at a commercial or non-commercial institution that contains the consumer id, transaction time, and transaction items. From transaction data such as supermarket transactions, sequential patterns can be found to determine the interrelationship between items or items. Data if further processed or analyzed will produce information or knowledge that is important and valuable as a support in decision making. This study aims to determine the consumption patterns owned by customers and provide information that can be used in determining the layout of new store shelves. The SPADE algorithm is an algorithm for finding sequential patterns to break down the main problem into sub-problems that can be solved separately. Based on the result obtained it can be concluded that the application of the SPADE algorithm has the highest minimum support value that can still form maximal frequent sequences is 29%. The highest minimum support value of the SPADE algorithm is 0.2% with a maximum minimum confidence value of 81% and the number of rules formed is 1,118 Rule, but confidence is taken 60% up so that there are only 15 Rule. Whereas the Apriori algorithm has the highest minimum support value that can still form the maximum frequent sequences is 25%. The highest minimum support value of the apriori algorithm which can still form a rule is 0.3% with a maximum value of 88% minimum confidence and the number of rules formed as many as 494 Rule, but confidence is taken 60% up so that there are only 29 Rule.
16
Polpinij, Jantima, Aditya Ghose, and Hoa Khanh Dam. "Mining business rules from business process model repositories." Business Process Management Journal 21, no. 4 (July 6, 2015): 820–36. http://dx.doi.org/10.1108/bpmj-01-2014-0004.
Full textAbstract:
Purpose – Business process has become the core assets of many organizations and it becomes increasing common for most medium to large organizations to have collections of hundreds or even thousands of business process models. The purpose of this paper is to explore an alternative dimension to process mining in which the objective is to extract process constraints (or business rules) as opposed to business process models. It also focusses on an alternative data set – process models as opposed to process instances (i.e. event logs). Design/methodology/approach – The authors present a new method of knowledge discovery to find business activity sequential patterns embedded in process model repositories. The extracted sequential patterns are considered as business rules. Findings – The authors find significant knowledge hidden in business processes model repositories. The hidden knowledge is considered as business rules. The business rules extracted from process models are significant and valid sequential correlations among business activities belonging to a particular organization. Such business rules represent business constraints that have been encoded in business process models. Experimental results have indicated the effectiveness and accuracy of the approach in extracting business rules from repositories of business process models. Social implications – This research will assist organizations to extract business rules from their existing business process models. The discovered business rules are very important for any organization, where rules can be used to help organizations better achieve goals, remove obstacles to market growth, reduce costly mistakes, improve communication, comply with legal requirements, and increase customer loyalty. Originality/value – There has very been little work in mining business process models as opposed to an increasing number of very large collections of business process models. This work has filled this gap with the focus on extracting business rules.
17
Manurung, Oktaviani, and Penda Sudarto Hasugian. "Analisa Algoritma Apriori Untuk Peminjaman Buku Pada Perpustakaan SMA 1 Silima Pungga-Pungga Parongil." remik 4, no. 1 (December 21, 2019): 154–60. http://dx.doi.org/10.33395/remik.v4i1.10445.
Full textAbstract:
ABSTRACT
The library has the role of helping students to love reading books. The availability of books in various fields motivates students to come to visit the library, students can read or borrow library books. For this reason, library officers apply the rules for visiting the library. The Apriori algorithm is a part of data mining, namely the search for high frequency patterns such as activities that often appear simultaneously. The pattern that will be analyzed is the pattern of borrowing any books that are often borrowed so that librarians know the information of books that are often borrowed. With the application of a priori algorithms, book data is processed to produce a book borrowing pattern. After all the high frequency patterns were found, then association rules were found that met the minimum requirements for associative confidence A → B minimum confidence = 25%. Rules for sequential final association based on minimum support and minimum confidence, if borrowing an IPA, then borrowing MTK Support = 15%, Confidence = 42.8%.
Keywords:Patterns of borrow of books, Library, Apriori Algorithms
18
Zhang, Mengjiao, Tiantian Xu, Zhao Li, Xiqing Han, and Xiangjun Dong. "e-HUNSR: An Efficient Algorithm for Mining High Utility Negative Sequential Rules." Symmetry 12, no. 8 (July 23, 2020): 1211. http://dx.doi.org/10.3390/sym12081211.
Full textAbstract:
As an important technology in computer science, data mining aims to mine hidden, previously unknown, and potentially valuable patterns from databases.High utility negative sequential rule (HUNSR) mining can provide more comprehensive decision-making information than high utility sequential rule (HUSR) mining by taking non-occurring events into account. HUNSR mining is much more difficult than HUSR mining because of two key intrinsic complexities. One is how to define the HUNSR mining problem and the other is how to calculate the antecedent’s local utility value in a HUNSR, a key issue in calculating the utility-confidence of the HUNSR. To address the intrinsic complexities, we propose a comprehensive algorithm called e-HUNSR and the contributions are as follows. (1) We formalize the problem of HUNSR mining by proposing a series of concepts. (2) We propose a novel data structure to store the related information of HUNSR candidate (HUNSRC) and a method to efficiently calculate the local utility value and utility of HUNSRC’s antecedent. (3) We propose an efficient method to generate HUNSRC based on high utility negative sequential pattern (HUNSP) and a pruning strategy to prune meaningless HUNSRC. To the best of our knowledge, e-HUNSR is the first algorithm to efficiently mine HUNSR. The experimental results on two real-life and 12 synthetic datasets show that e-HUNSR is very efficient.
19
Stallman, Elizabeth L., Kent C. Berridge, and Matthew T. Colonnese. "Ontogeny of Action Syntax in Altricial and Precocial Rodents: Grooming Sequences of Rat and Guinea Pig Pups." Behaviour 133, no. 15-16 (1996): 1165–95. http://dx.doi.org/10.1163/156853996x00350.
Full textAbstract:
AbstractBehavior occurs as coordinated patterns of serial order, with rules of 'action syntax'. Grooming behavior of adult rodents provides several striking examples of action syntax rules. The most stereotyped of these is a 'syntactic chain' pattern, which organizes up to 25 facial strokes and licking movements into a predictable sequence. This pattern previously has been found to be emitted by diverse rodent species from all major suborders: myomorph, sciuromorph, and caviomorph. In this study, we compared the postnatal ontogeny of grooming syntax in two rodent species: rat versus guinea pig. Rats are relatively altricial at birth, whereas guinea pigs are precocial. A day-by-day study of the fine-grain structure of sequential patterns was carried out during the first three weeks after birth, using slow-motion videoanalyses and a choreographic notation system for scoring behavioral grooming sequences. The results showed that substantial action syntax rules exist in guinea pig grooming even on the day of birth. For guinea pigs, postnatal grooming syntax development was limited to minor increments in the strength of the syntactic pattern and in postural control. By contrast, for rat pups, action syntax did not begin to appear until the second postnatal week, and developed gradually into the third week. The development of rodent syntactic patterns in both species appeared to be independent of the maturation of the movements that composed the pattern. Our results indicate that action syntax rules develop as hierarchical entities independent from constituent movements.
20
Ding, Zhi, Xiaohan Liao, Fenzhen Su, and Dongjie Fu. "Mining Coastal Land Use Sequential Pattern and Its Land Use Associations Based on Association Rule Mining." Remote Sensing 9, no. 2 (January 29, 2017): 116. http://dx.doi.org/10.3390/rs9020116.
Full textAbstract:
Abstract: Research on the land use of the coastal zone in the sea–land direction will not only reveal its land use distribution, but may also indicate the interactions between inland land use and the ocean through associations between inland land use and seaward land use indirectly. However, in the existing research, few have paid attention to the land use in sea–land direction, let alone the sequential relationship between land-use types. The sequential relationship would be useful in land use planning and rehabilitation of the landscape in the sea–land direction, and the association between land-use types, particularly the inland land use and seaward land use, is not discussed. Therefore, This study presents a model named ARCLUSSM (Association Rules-based Coastal Land use Spatial Sequence Model) to mine the sequential pattern of land use with interesting associations in the sea–land direction of the coastal zone. As a case study, the typical coastal zone of Bohai Bay and the Yellow River delta in China was used. The results are as follows: firstly, 27 interesting association patterns of land use in the sea–land direction of the coastal zone were mined easily. Both sequential relationship and distance between land-use types for 27 patterns among six land-use types were mined definitely, and the sequence of the six land-use types tended to be tidal flat > shrimp pond > reservoir/artificial pond > settlement > river > dry land in sea–land direction. These patterns would offer specific support for land-use planning and rehabilitation of the coastal zone. There were 19 association patterns between seaward and landward land-use types. These patterns showed strong associations between seaward and landward land-use types. It indicated that the landward land use might have some impacts on the seaward land use, or in the other direction, which may help to reveal the interactions between inland land use and the ocean. Thus, the ARCLUSSM was an efficient tool to mine the sequential relationship and distance between land-use types with interesting association rules in the sea–land direction, which would offer practicable advice to appropriate coastal zone management and planning, and might reveal the interactions between inland land use and the ocean.
21
Di Martino, Beniamino, and Antonio Esposito. "Automatic Dynamic Data Structures Recognition to Support the Migration of Applications to the Cloud." International Journal of Grid and High Performance Computing 7, no. 3 (July 2015): 1–22. http://dx.doi.org/10.4018/ijghpc.2015070101.
Full textAbstract:
The work presented in this manuscript describes a methodology for the recognition of Dynamic Data structures, with a focus on Queues, Pipes and Lists. The recognition of such structures is used as a basis for the mapping of sequential code to Cloud Services, in order to support the semi-automatic restructuring of source software. The goal is to develop a complete methodology and a framework based on it to ease the efforts needed to port native applications to a Cloud Platform and simplify the relative complex processes. In order to achieve such an objective, the proposed technique exploits an intermediate representation of the code, consisting in parallel Skeletons and Cloud Patterns. Logical inference rules act on a knowledge base, built during the analysis of the source code, to guide the recognition and mapping processes. Both the inference rules and knowledge base are expressed in Prolog. A prototype tool for the automatic analysis of sequential source code and its mapping to a Cloud Pattern is also presented.
22
TSAI, CHIEH-YUAN, CHIH-CHUNG LO, and CHAO-WEN LIN. "A TIME-INTERVAL SEQUENTIAL PATTERN CHANGE DETECTION METHOD." International Journal of Information Technology & Decision Making 10, no. 01 (January 2011): 83–108. http://dx.doi.org/10.1142/s0219622011004233.
Full textAbstract:
Several studies have focused on mining changes in different time-period databases. Analyzing these change behaviors provides useful information for managers to develop better marketing strategies and decision making. Although some researchers have developed efficient methods for association rule change detection, no attempt has been made to analyze time-interval sequential pattern changes in databases collected over time. Therefore, this research proposes a time-interval sequential pattern change detection framework to derive the change trends in customer behaviors in two periods. First, two time-interval sequential pattern sets are generated from two time-period databases respectively using the proposed DTI-Apriori algorithm. Different from previous mining methods that require users to manually define a set of time-interval ranges in advance, the DTI-Apriori algorithm automatically arranges the time-interval range and then generates time-interval sequential patterns. The degree of change for each pair of time-interval sequential patterns from different time periods is evaluated next. Based on the degree of change, a time-interval sequential pattern is clarified as one of the following three change types: an emerging time-interval sequential pattern, an unexpected time-interval sequential pattern, or an added/perished time-interval sequential pattern. Significant change patterns are returned to users for further analysis if the degree of change is large enough.
23
Jiang, Xiaoqi, Tiantian Xu, and Xiangjun Dong. "Campus Data Analysis Based on Positive and Negative Sequential Patterns." International Journal of Pattern Recognition and Artificial Intelligence 33, no. 05 (April 8, 2019): 1959016. http://dx.doi.org/10.1142/s021800141959016x.
Full textAbstract:
Campus data analysis is becoming increasingly important in mining students’ behavior. The consumption data of college students is an important part of the campus data, which can reflect the students’ behavior to a great degree. A few methods have been used to analyze students’ consumption data, such as classification, association rules, clustering, decision trees, time series, etc. However, they do not use the method of sequential patterns mining, which results in some important information missing. Moreover, they only consider the occurring (positive) events but do not consider the nonoccurring (negative) events, which may lead to some important information missing. So this paper uses a positive and negative sequential patterns mining algorithm, called NegI-NSP, to analyze the consumption data of students. Moreover, we associate students’ consumption data with their academic grades by adding the students’ academic grades into sequences to analyze the relationship between the students’ academic grades and their consumptions. The experimental results show that the students’ academic performance has significant correlation with the habits of having breakfast regularly.
24
Browne, P., S. Morgan, J. Bahnisch, and S. Robertson. "Discovering patterns of play in netball with network motifs and association rules." International Journal of Computer Science in Sport 18, no. 1 (July 1, 2019): 64–79. http://dx.doi.org/10.2478/ijcss-2019-0004.
Full textAbstract:
Abstract In netball, analysis of the movement of players and the ball across different court locations can provide information about trends otherwise hidden. This study aimed to develop a method to discover latent passing patterns in women’s netball. Data for both pass location and playing position were collected from centre passes during selected games in the 2016 Trans-Tasman Netball Championship season and 2017 Australian National Netball League. A motif analysis was used to characterise passing-sequence observations. This revealed that the most frequent, sequential passing style from a centre pass was the “ABCD” motif in an alphabetical system, or in a positional system “Centre–Goal Attack–Wing Attack–Goal Shooter” and rarely was the ball passed back to the player it was received from. An association rule mining was used to identify frequent ball movement sequences from a centre pass play. The most confident rule flowed down the right-hand side of the court, however seven of the ten most confident rules demonstrated a preference for ball movement down the left-hand side of the court. These results can offer objective insight into passing sequences, and potentially inform team strategy and tactics. This method can also be generalised to other invasion sports.
25
Berridge, Kent C. "Comparative Fine Structure of Action: Rules of Form and Sequence in the Grooming Patterns of Six Rodent Species." Behaviour 113, no. 1-2 (1990): 21–56. http://dx.doi.org/10.1163/156853990x00428.
Full textAbstract:
AbstractThe phylogenetic constancy of a set of syntactic patterning rules for grooming was examined in six rodent species: guinea pig, Belding's ground squirrel, gerbil, hamster, rat, mouse. Species were chosen to allow comparisons of separate suborders of Rodentia (Hystricomorpha, Sciuromorpha, Myomorpha) and of separate families within suborders (Cricetidae and Muridae). Each species was examined for possession of the syntactic patterns of chaining, transition reciprocity, sequential stereotypy, and hierarchical clustering. These syntactic patterns were detected and quantified using videoanalysis, graphic notation, and a variety of computer-assisted action coding and analysis techniques. Each syntactic pattern or sequencing rule mentioned above was found to obtain in all six of the species tested. The wide applicability of these rules suggests that they reflect a fundamental feature of neurobehavioral organization, which was established relatively early in rodent evolution. Syntactic organization appears to be a basic property of action production by mammalian brains. The techniques used in this study also allowed a quantitative comparison to be made across species of syntactic pattern strength, form, stereotypy, and timing parameters. This comparison showed that many differences in behavioral patterns among species could be explained by one of two principles. The first explanatory principle was phylogenetic relationship: the behavioral traits of species from within a single family tended to be more similar than were traits of species from separate families, and species from a single suborder tended to be more similar than species from separate suborders. The second principle, which applied especially to temporal parameters, was programmed allometric control by physical size. The timing of patterns (for example, the cycle duration of certain highly stereotyped forepaw strokes performed on the face) was related to the average size of the species by an allometric power function. The nature of these syntactic patterns and of the rules that generate them, the nature of their neural substrates, and the origin of parameters differences between species, is discussed.
26
Ykhlef, Mourad, and Hebah ElGibreen. "Mining Pharmacy Database Using Evolutionary Genetic Algorithm." International Journal of Electronics and Telecommunications 56, no. 4 (November 1, 2010): 427–32. http://dx.doi.org/10.2478/v10177-010-0058-4.
Full textAbstract:
Mining Pharmacy Database Using Evolutionary Genetic AlgorithmMedication management is an important process in pharmacy field. Prescribing errors occur upstream in the process, and their effects can be perpetuated in subsequent steps. Prescription errors are an important issue for which conflicts with another prescribed medicine could cause severe harm for a patient. In addition, due to the shortage of pharmacists and to contain the cost of healthcare delivery, time is also an important issue. Former knowledge of prescriptions can reduce the errors, and discovery of such knowledge requires data mining techniques, such as Sequential Pattern. Moreover, Evolutionary Algorithms, such as Genetic Algorithm (GA), can find good rules in short time, thus it can be used to discover the Sequential Patterns in Pharmacy Database. In this paper GA is used to assess patient prescriptions based on former knowledge of series of prescriptions in order to extract sequenced patterns and predict unusual activities to reduce errors in timely manner.
27
Stobbe, Nina, Gesche Westphal-Fitch, Ulrike Aust, and W. Tecumseh Fitch. "Visual artificial grammar learning: comparative research on humans, kea ( Nestor notabilis ) and pigeons ( Columba livia )." Philosophical Transactions of the Royal Society B: Biological Sciences 367, no. 1598 (July 19, 2012): 1995–2006. http://dx.doi.org/10.1098/rstb.2012.0096.
Full textAbstract:
Artificial grammar learning (AGL) provides a useful tool for exploring rule learning strategies linked to general purpose pattern perception. To be able to directly compare performance of humans with other species with different memory capacities, we developed an AGL task in the visual domain. Presenting entire visual patterns simultaneously instead of sequentially minimizes the amount of required working memory. This approach allowed us to evaluate performance levels of two bird species, kea ( Nestor notabilis ) and pigeons ( Columba livia ), in direct comparison to human participants. After being trained to discriminate between two types of visual patterns generated by rules at different levels of computational complexity and presented on a computer screen, birds and humans received further training with a series of novel stimuli that followed the same rules, but differed in various visual features from the training stimuli. Most avian and all human subjects continued to perform well above chance during this initial generalization phase, suggesting that they were able to generalize learned rules to novel stimuli. However, detailed testing with stimuli that violated the intended rules regarding the exact number of stimulus elements indicates that neither bird species was able to successfully acquire the intended pattern rule. Our data suggest that, in contrast to humans, these birds were unable to master a simple rule above the finite-state level, even with simultaneous item presentation and despite intensive training.
28
Ding, Wentao, Guanji Gao, Linfeng Shi, and Yuzhong Qu. "A Pattern-Based Approach to Recognizing Time Expressions." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 6335–42. http://dx.doi.org/10.1609/aaai.v33i01.33016335.
Full textAbstract:
Recognizing time expressions is a fundamental and important task in many applications of natural language understanding, such as reading comprehension and question answering. Several newest state-of-the-art approaches have achieved good performance on recognizing time expressions. These approaches are black-boxed or based on heuristic rules, which leads to the difficulty in understanding the temporal information. On the contrary, classic rule-based or semantic parsing approaches can capture rich structural information, but their performances on recognition are not so good. In this paper, we propose a pattern-based approach, called PTime, which automatically generates and selects patterns for recognizing time expressions. In this approach, time expressions in training text are abstracted into type sequences by using fine-grained token types, thus the problem is transformed to select an appropriate subset of the sequential patterns. We use the Extended Budgeted Maximum Coverage (EBMC) model to optimize the pattern selection. The main idea is to maximize the correct token sequences matched by the selected patterns while the number of the mistakes should be limited by an adjustable budget. The interpretability of patterns and the adjustability of permitted number of mistakes make PTime a very promising approach for many applications. Experimental results show that PTime achieves a very competitive performance as compared with existing state-of-the-art approaches.
29
Shim, Beomsoo, Keunho Choi, and Yongmoo Suh. "CRM strategies for a small-sized online shopping mall based on association rules and sequential patterns." Expert Systems with Applications 39, no. 9 (July 2012): 7736–42. http://dx.doi.org/10.1016/j.eswa.2012.01.080.
Full text
30
Rondal, Jean A. "Natural morphosyntax." Cognitive Linguistic Studies 2, no. 2 (December 31, 2015): 181–204. http://dx.doi.org/10.1075/cogls.2.2.01ron.
Full textAbstract:
Fluent speakers do not appear to have conscious knowledge of the linguistic categories and declarative rules that linguists use to describe grammar and that most psycholinguists have adopted for explaining language functioning. The implication derived in this paper is that these categories and rules are deprived of psychological reality. It is proposed that a psychologically real morphosyntax is concerned with sentence surface. The pragmatic framework and the semantic relational matrix at the onset of sentence production are converted directly into syntagmatic patterns, flexibly distributed along the sentence line. These patterns are reflected in probabilistic associations between words and sequences of words. Natural morphosyntax is learned incidentally through implicit procedural learning. Children extract frequent syntagmatic patterns from adapted adult input. The resulting knowledge is stored in procedural memory. The cortico-striatal -cerebellar system of the brain has the computational power necessary to deal with sentence sequential patterning and associative regularities.
31
NAKAMURA, AKIRA. "SOME NOTES ON PARALLEL COORDINATE GRAMMARS." International Journal of Pattern Recognition and Artificial Intelligence 09, no. 05 (October 1995): 753–61. http://dx.doi.org/10.1142/s0218001495000304.
Full textAbstract:
In a coordinate grammar, the rewriting rules replace sets of symbols having given coordinates by sets of symbols whose coordinates are given functions of the coordinates of the original symbols. Usually, at each step of a derivation, only one rule is applied and only one instance of its left hand side is rewritten. This type is referred to sequential grammars. As a counterpart of this grammar, parallel coordinate grammars are defined as generalized parallel isometric grammars. In the parallel grammars, the rewriting rule are used in parallel in a derivation application. The paper discusses some properties of parallel coordinate grammars and examines a relationship between the sequential coordinate grammars and parallel ones.
32
Yao, Liguo, Haisong Huang, and Shih-Huan Chen. "Product Quality Detection through Manufacturing Process Based on Sequential Patterns Considering Deep Semantic Learning and Process Rules." Processes 8, no. 7 (June 28, 2020): 751. http://dx.doi.org/10.3390/pr8070751.
Full textAbstract:
Companies accumulate a large amount of production process data during product manufacturing. Sequence data from the mining production process can enable a company to evaluate the manufacturing process, to find the key factors affecting product quality, and to improve product quality. However, the production process mainly exists in the form of text. To solve this problem, we propose a novel frequent pattern mining algorithm (EABMC) based on the text context semantics and rules of the manufacturing process to remove redundant sequences and to obtain good mining results. In this algorithm, first, we use embeddings from language models (ELMo ) to improve the process of text similarity matching and to classify similar semantic processes into one class. Then, the manufacturing process unit (MPU) is proposed by extracting the characteristics of manufacturing process data according to the constraints of the manufacturing process and other conditions. The above two steps cause the complex manufacturing process sequence to merge and simplify. Once again, a frequent pattern mining algorithm (CloFAST) is used to explore the important manufacturing process relationships behind a large amount of manufacturing data. In addition, taking the data from a production enterprise in Guizhou Province as an example, the validity of the method is verified. Compared with other methods, this method is shown to have greater mining efficiency and better results and can find out the key factors that affect product quality, especially for text data.
33
Qiu, Ping, Long Zhao, Weiyang Chen, Tiantian Xu, and Xiangjun Dong. "Mining negative sequential patterns from infrequent positive sequences with 2-level multiple minimum supports." Filomat 32, no. 5 (2018): 1875–85. http://dx.doi.org/10.2298/fil1805875q.
Full textAbstract:
Negative sequential patterns (NSP) referring to both occurring items (positive items) and nonoccurring items (negative items) play a very important role in many real applications. Very few methods have been proposed to mine NSP and most of them only mine NSP from frequent positive sequences, not from infrequent positive sequences (IPS). In fact, many useful NSP can be mined from IPS, just like many useful negative association rules can be obtained from infrequent itemsets. e-NSPFI is a method to mine NSP from IPS, but its constraint is very strict to IPS and many useful NSP would be missed. In addition, e-NSPFI only uses a single minimum support, which implicitly assumes that all items in the database are of the similar frequencies. In order to solve the above problems and optimize NSP mining, a 2-level multiple minimum supports (2-LMMS) constraint to IPS is proposed in this paper. Firstly, we design two minimum supports constraints to mine frequent and infrequent positive sequences. Secondly, we use Select Actionable Pattern (SAP) method to select actionable NSP. Finally, we propose a corresponding algorithm msNSPFI to mine actionable NSP from IPS with 2-LMMS. Experiment results show that msNSPFI is very efficient for mining actionable NSP.
34
Shaked, D., O. Yaron, and N. Kiryati. "Deriving Stopping Rules for the Probabilistic Hough Transform by Sequential Analysis." Computer Vision and Image Understanding 63, no. 3 (May 1996): 512–26. http://dx.doi.org/10.1006/cviu.1996.0038.
Full text
35
Mostafa, Hesham, and Giacomo Indiveri. "Sequential Activity in Asymmetrically Coupled Winner-Take-All Circuits." Neural Computation 26, no. 9 (September 2014): 1973–2004. http://dx.doi.org/10.1162/neco_a_00619.
Full textAbstract:
Understanding the sequence generation and learning mechanisms used by recurrent neural networks in the nervous system is an important problem that has been studied extensively. However, most of the models proposed in the literature are either not compatible with neuroanatomy and neurophysiology experimental findings, or are not robust to noise and rely on fine tuning of the parameters. In this work, we propose a novel model of sequence learning and generation that is based on the interactions among multiple asymmetrically coupled winner-take-all (WTA) circuits. The network architecture is consistent with mammalian cortical connectivity data and uses realistic neuronal and synaptic dynamics that give rise to noise-robust patterns of sequential activity. The novel aspect of the network we propose lies in its ability to produce robust patterns of sequential activity that can be halted, resumed, and readily modulated by external input, and in its ability to make use of realistic plastic synapses to learn and reproduce the arbitrary input-imposed sequential patterns. Sequential activity takes the form of a single activity bump that stably propagates through multiple WTA circuits along one of a number of possible paths. Because the network can be configured to either generate spontaneous sequences or wait for external inputs to trigger a transition in the sequence, it provides the basis for creating state-dependent perception-action loops. We first analyze a rate-based approximation of the proposed spiking network to highlight the relevant features of the network dynamics and then show numerical simulation results with spiking neurons, realistic conductance-based synapses, and spike-timing dependent plasticity (STDP) rules to validate the rate-based model.
36
MONDRAGON, OSCAR A., ANN Q. GATES, STEVE ROACH, HUMBERTO MENDOZA, and OLEG SOKOLSKY. "GENERATING PROPERTIES FOR RUNTIME MONITORING FROM SOFTWARE SPECIFICATION PATTERNS." International Journal of Software Engineering and Knowledge Engineering 17, no. 01 (February 2007): 107–26. http://dx.doi.org/10.1142/s021819400700315x.
Full textAbstract:
This paper presents an approach to support run-time verification of software systems that combines two existing tools, Prospec and Java-MaC, into a single framework. Prospec can be used to clarify natural language specifications for sequential, concurrent, and nondeterministic behavior. In addition, Prospec assists the user in reading, writing, and understanding formal specifications through the use of property patterns and visual abstractions. Prospec automatically generates specifications written in Future Interval Logic (FIL). Java-MaC monitors Java programs at runtime to ensure adherence to a set of formally specified properties. Safety properties of a program are specified in the formal language Meta-Event Definition Language (MEDL). Java-MaC generates runtime components from specifications. The components are used to instrument the target program and determine whether the execution of the program violates any of the safety properties. This paper describes an algorithm for translating FIL formulas into MEDL formulas. It provides the transformation rules used by this algorithm, and it demonstrates the general correctness of the translation.
37
Leski, Jacek M., and Marian P. Kotas. "Linguistically Defined Clustering of Data." International Journal of Applied Mathematics and Computer Science 28, no. 3 (September 1, 2018): 545–57. http://dx.doi.org/10.2478/amcs-2018-0042.
Full textAbstract:
Abstract This paper introduces a method of data clustering that is based on linguistically specified rules, similar to those applied by a human visually fulfilling a task. The method endeavors to follow these remarkable capabilities of intelligent beings. Even for most complicated data patterns a human is capable of accomplishing the clustering process using relatively simple rules. His/her way of clustering is a sequential search for new structures in the data and new prototypes with the use of the following linguistic rule: search for prototypes in regions of extremely high data densities and immensely far from the previously found ones. Then, after this search has been completed, the respective data have to be assigned to any of the clusters whose nuclei (prototypes) have been found. A human again uses a simple linguistic rule: data from regions with similar densities, which are located exceedingly close to each other, should belong to the same cluster. The goal of this work is to prove experimentally that such simple linguistic rules can result in a clustering method that is competitive with the most effective methods known from the literature on the subject. A linguistic formulation of a validity index for determination of the number of clusters is also presented. Finally, an extensive experimental analysis of benchmark datasets is performed to demonstrate the validity of the clustering approach introduced. Its competitiveness with the state-of-the-art solutions is also shown.
38
Can, Umit, and Bilal Alatas. "Automatic Mining of Quantitative Association Rules with Gravitational Search Algorithm." International Journal of Software Engineering and Knowledge Engineering 27, no. 03 (April 2017): 343–72. http://dx.doi.org/10.1142/s0218194017500127.
Full textAbstract:
The classical optimization algorithms are not efficient in solving complex search and optimization problems. Thus, some heuristic optimization algorithms have been proposed. In this paper, exploration of association rules within numerical databases with Gravitational Search Algorithm (GSA) has been firstly performed. GSA has been designed as search method for quantitative association rules from the databases which can be regarded as search space. Furthermore, determining the minimum values of confidence and support for every database which is a hard job has been eliminated by GSA. Apart from this, the fitness function used for GSA is very flexible. According to the interested problem, some parameters can be removed from or added to the fitness function. The range values of the attributes have been automatically adjusted during the time of mining of the rules. That is why there is not any requirements for the pre-processing of the data. Attributes interaction problem has also been eliminated with the designed GSA. GSA has been tested with four real databases and promising results have been obtained. GSA seems an effective search method for complex numerical sequential patterns mining, numerical classification rules mining, and clustering rules mining tasks of data mining.
39
Wenninger, Sebastian, Daniel Link, and Martin Lames. "Data Mining in Elite Beach Volleyball – Detecting Tactical Patterns Using Market Basket Analysis." International Journal of Computer Science in Sport 18, no. 2 (September 1, 2019): 1–19. http://dx.doi.org/10.2478/ijcss-2019-0010.
Full textAbstract:
Abstract Sports coaches today have access to a growing amount of information that describes the performance of their players. Methods such as data mining have become increasingly useful tools to deal with the analytical demands of these high volumes of data. In this paper, we present a sports data mining approach using a combination of sequential association rule mining and clustering to extract useful information from a database of more than 400 high level beach volleyball games gathered at FIVB events in the years from 2013 to 2016 for both men and women. We regard each rally as a sequence of transactions including the tactical behaviours of the players. Use cases of our approach are shown by its application on the aggregated data for both genders and by analyzing the sequential patterns of a single player. Results indicate that sequential rule mining in conjunction with clustering can be a useful tool to reveal interesting patterns in beach volleyball performance data.
40
SAOUDI, A., K. RANGARAJAN, and V. R. DARE. "FINITE IMAGES GENERATED BY GL-SYSTEMS." International Journal of Pattern Recognition and Artificial Intelligence 03, no. 03n04 (December 1989): 459–67. http://dx.doi.org/10.1142/s0218001489000346.
Full textAbstract:
In this paper, we introduce a new device called GL-systems (i.e. Grammar-Lindenmayer systems) for generating finite images. GL-systems are sequential/parallel systems in which the horizontal rules form a Chomskian grammar and the vertical rules form a DTOL system. We obtain hierarchical results of various types of GL-systems. We study some properties like, closure properties, combinatorial results, pumping lemma and decidability results. An algebraic characterization for GL-systems (with variation) is presented.
41
P, Haritha, Sree Devi M, Ravali K, and Manoj Pruthvi M. "A survey for acquiring frequent and sequential items in E-commerce sites." International Journal of Engineering & Technology 7, no. 1.1 (December 21, 2017): 273. http://dx.doi.org/10.14419/ijet.v7i1.1.9484.
Full textAbstract:
Large amounts of data has made available because of the increase in e-commerce industry. Data has high significance and also important for everyone. Hundreds of websites are being deployed and each site offers millions of products. In addition to this there are several types of input forms. Different sites have different input item collection. This means that there is a substantial amount of information being provided resulting in information overload and in turn results in reduced customer satisfaction and interest. This huge amount of data needs to get processed so that we can able to extract the useful information. From this useful information we can able to increase customer interest, satisfaction along with sales of e-commerce sites. Presenting frequent and sequential patterns in e-commerce sites results in increase of sales of products without delay. Different association rule mining techniques and sequential rule mining techniques can be used for different sets of input forms in order to generate frequent and sequential patterns. This paper discusses various algorithms using techniques such as association rule mining, sequence rule mining proposed for mining frequent and sequential items.
42
Hamasuna, Yukihiro, and Yasunori Endo. "On Sequential Cluster Extraction Based onL1-Regularized Possibilisticc-Means." Journal of Advanced Computational Intelligence and Intelligent Informatics 19, no. 5 (September 20, 2015): 655–61. http://dx.doi.org/10.20965/jaciii.2015.p0655.
Full textAbstract:
Sequential cluster extraction algorithms are useful clustering methods that extract clusters one by one without the number of clusters having to be determined in advance. Typical examples of these algorithms are sequential hardc-means (SHCM) and possibilistic clustering (PCM) based algorithms. Two types ofL1-regularized possibilistic clustering are proposed to induce crisp and possibilistic allocation rules and to construct a novel sequential cluster extraction algorithm. The relationship between the proposed method and SHCM is also discussed. The effectiveness of the proposed method is verified through numerical examples. Results show that the entropy-based method yields better results for the Rand Index and the number of extracted clusters.
43
Vu, Huy Quan, Gang Li, Rob Law, and Yanchun Zhang. "Travel Diaries Analysis by Sequential Rule Mining." Journal of Travel Research 57, no. 3 (February 1, 2017): 399–413. http://dx.doi.org/10.1177/0047287517692446.
Full textAbstract:
Because of the inefficiency in analyzing the comprehensive travel data, tourism managers are facing the challenge of gaining insights into travelers’ behavior and preferences. In most cases, existing techniques are incapable of capturing the sequential patterns hidden in travel data. To address these issues, this article proposes to analyze the travelers’ behavior through geotagged photos and sequential rule mining. Travel diaries, constructed from the photo sequences, can capture comprehensive travel information, and then sequential patterns can be discovered to infer the potential destinations. The effectiveness of the proposed framework is demonstrated in a case study of Australian outbound tourism, using a data set of more than 890,000 photos from 3,623 travelers. The introduced framework has the potential to benefit tourism researchers and practitioners from capturing and understanding the behaviors and preferences of travelers. The findings can support destination-marketing organizations (DMOs) in promoting appropriate destinations to prospective travelers.
44
Kuchuganov, A. V., D. R. Kasimov, and V. N. Kuchuganov. "Modeling of reasoning when searching for objects in images." Vestnik Udmurtskogo Universiteta. Matematika. Mekhanika. Komp'yuternye Nauki 30, no. 3 (September 2020): 497–512. http://dx.doi.org/10.35634/vm200310.
Full textAbstract:
Visual patterns, for example, handwritten letters or objects of aerospace observations, are highly variable. The high variety and large volume of unstructured information lead to the need for complex and resource-intensive calculations. Unfortunately, image analysis approaches based on the domain ontology do not specify any method for automatic selection of criteria (features) and decision-making rules. Insufficient structuredness of cases and a large variability of object images lead to a rapid growth of the case base, which significantly reduces the performance of the decision support system. The article proposes an approach to the structural analysis of images, which consists in sequential refinement of objects' features and weakening of interpretation rules during an iterative search of facts using the ontology of images represented as attributed graphs of relationships between elements of objects. The algorithm of reasoning on graphic information consists in the sequence of task (functional) actions necessary for processing and analyzing the image in accordance with the task, the actions of the system to prepare conditions for their implementation, as well as to organize and manage the reasoning process.
45
Marler, Peter, and John C. Mitani. "A Phonological Analysis of Male Gibbon Singing Behavior." Behaviour 109, no. 1-2 (1989): 20–45. http://dx.doi.org/10.1163/156853989x00141.
Full textAbstract:
AbstractAcoustic analyses and experimental field playbacks were conducted to investigate the nature and communicative significance of the phonological structure and organization of male gibbon (Hylobates agilis) songs. Males use a limited number of spectrographically discrete elements or note types to construct songs. A classification of these note types was produced initially through a visual sorting process using gross spectral and temporal features. Measurement of single acoustic variables and a digital sound program, which compared the two-dimensional cross-correlation values of note spectrograms, were employed to check the results of the qualitative sorting procedure. The sequential organization of notes composing songs was examined by tabulating the frequencies of occurrence of each note type in different positions and the transition probabilities between note types. These analyses revealed that songs are formed within a framework of rules, which define regular patterns in the placement and order of note types. To investigate whether the gibbons employ these rules in a biologically meaningful fashion, a field playback experiment was conducted comparing the responses of animals to normal and phonologically rearranged songs. Although the gibbons identify rearranged songs as conspecific territorial signals, responses to playbacks suggested that the hypothesized rules generating songs are communicatively significant; male gibbons responded in a qualitatively different fashion to normal and phonologically rearranged songs. These results are evaluated in light of other studies of animal vocal communication.
46
Kochar, Barjesh, and Rajender Singh Chhillar. "A Novel RFID Data Mining System: Integration of Effective Sequential Pattern Mining and Fuzzy Rules Generation Techniques." International Journal of Wireless Information Networks 18, no. 4 (June 10, 2011): 309–18. http://dx.doi.org/10.1007/s10776-011-0151-3.
Full text
47
Efremova, I. N., V. V. Efremov, and N. A. Emelianova. "A METHOD OF SEQUENTIAL SEARCHING OF OCCURANCES IN TEXT WITH THE ACCOUNT OF POSSIBLE COLLISIONS." Proceedings of the Southwest State University 21, no. 4 (August 28, 2017): 68–74. http://dx.doi.org/10.21869/2223-1560-2017-21-4-68-74.
Full textAbstract:
One of the fundamental tasks of modern computer information systems is processing of symbol information, the amount of which prevails in the total amount of information. At present, rules-based approach is effectively applied to the tasks of processing symbol information. The paper deals with the peculiarities of text search applying rules-based approach. The main essence of the approach is to find pattern occurrences in the text and possible implementation of substitution (text modification). Meanwhile, when implementing search for occurrences, various kinds of collisions may arise. They should be taken into account to solve the set tasks correctly. For example, algorythms of sequential word matching can run into collisions which involve the possibility of skipping positions of pattern occurrence in a word with some structural peculiarities. The paper presents a method of searching taking into account possible collisions developed by the authors, as well as algorithmic and automatic models of the method. The developed method involves patterm markup and setting a sequence of its viewing in the form of algorithm diagram. Three algorythms (implementation variants) of the method have been developed. Algorithms differ in the possibility to carry out transition to this oк that position of the pattern and the text depending on the result of matching (equality or inequality of the current symbols of the patten and text). An automation model of the method has been developed. The proposed method of sequential matching with the pattern with collisions elimination increases the effectiveness of the computer system when implementing search procedures and symbol information processing. The method can be used in the systems of symbol information processing.
48
Phadnis, Milind A., and Matthew S. Mayo. "Group sequential design for time-to-event data using the concept of proportional time." Statistical Methods in Medical Research 29, no. 7 (October 1, 2019): 1867–90. http://dx.doi.org/10.1177/0962280219876313.
Full textAbstract:
Sequential monitoring of efficacy and safety is an important part of clinical trials. A Group Sequential design allows researchers to perform interim monitoring after groups of patients have completed the study. Statistical literature is well developed for continuous and binary outcomes and relies on asymptotic normality of the test statistic. However, in the case of time-to-event data, existing methods of sample size calculation are done either assuming proportional hazards or assuming exponentially distributed lifetimes. In scenarios where these assumptions are not true, as evidenced from historical data, these traditional methods are restrictive and cannot always be used. As interim monitoring is driven by ethical, financial, and administrative considerations, it is imperative that sample size calculations be done in an efficient manner keeping in mind the specific needs of a clinical trial with a time-to-event outcome. To address these issues, a novel group sequential design is proposed using the concept of Proportional Time. This method utilizes the generalized gamma ratio distribution to calculate the efficacy and safety boundaries and can be used for all distributions that are members of the generalized gamma family using an error spending approach. The design incorporates features specific to survival data such as loss to follow-up, administrative censoring, varying accrual times and patterns, binding or non-binding futility rules with or without skips, and flexible alpha and beta spending mechanisms. Three practical examples are discussed, followed by discussion of the important aspects of the proposed design.
49
COBLE, JEFFREY, DIANE J. COOK, and LAWRENCE B. HOLDER. "STRUCTURE DISCOVERY IN SEQUENTIALLY-CONNECTED DATA STREAMS." International Journal on Artificial Intelligence Tools 15, no. 06 (December 2006): 917–44. http://dx.doi.org/10.1142/s0218213006003041.
Full textAbstract:
Historically, data mining research has been focused on discovering sets of attributes that discriminate data entities into classes or association rules between attributes. In contrast, we are working to develop data mining techniques to discover patterns consisting of complex relationships between entities. Our research is particularly applicable to domains in which the data is event driven, such as counter-terrorism intelligence analysis. In this paper we describe an algorithm designed to operate over relational data received from a continuous stream. Our approach includes a mechanism for summarizing discoveries from previous data increments so that the globally best patterns can be computed by examining only the new data increment. We then describe a method by which relational dependencies that span across temporal increment boundaries can be efficiently resolved so that additional pattern instances, which do not reside entirely in a single data increment, can be discovered. We also describe a method for change detection using a measure of central tendency designed for graph data. We contrast two formulations of the change detection process and demonstrate the ability to identify salient changes along meaningful dimensions and recognize trends in a relational data stream.
50
Li, Cheng-Hsuan, Pei-Ling Tsai, Zhi-Yong Liu, Wen-Chieh Huang, and Pei-Jyun Hsieh. "Exploring Collaborative Problem Solving Behavioral Transition Patterns in Science of Taiwanese Students at Age 15 According to Mastering Levels." Sustainability 13, no. 15 (July 28, 2021): 8409. http://dx.doi.org/10.3390/su13158409.
Full textAbstract:
This study analyzed the collaborative problem solving (CPS) behavioral transition patterns of 53,859 Taiwanese students in science at age 15 by using an online Taiwanese CPS assessment that was designed according to the Programme for International Student Assessment 2015 CPS framework. Because of behavioral changes over the testing period, the CPS target skills that corresponded to the assessment items can be viewed as a CPS behavioral sequence. Hence, a lag sequential analysis was applied to explore the significance of the interactions among the CPS skills. The behavioral sequence is coded according to the level of mastery (0, 1, or 2) of items. The CPS transition patterns were analyzed in three gaps, namely the gender gap, the urban–rural gap, and the achievement gap. The findings showed that “Monitoring and repairing the shared understanding” was a crucial CPS skill in science. Moreover, the female students who would follow rules of engagement effectively exhibited higher scores than male students did in monitoring the results of their actions and evaluating their success in solving the problem. No obvious differences were observed in the urban–rural gap, whereas differences were observed in the achievement gap.