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Journal articles on the topic 'Sequential Association Rules Mining'

Author: Grafiati

Published: 11 December 2022

Last updated: 26 January 2023

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1

Wang, Ling, Lingpeng Gui, and Peipei Xu. "Incremental sequential patterns for multivariate temporal association rules mining." Expert Systems with Applications 207 (November 2022): 118020. http://dx.doi.org/10.1016/j.eswa.2022.118020.

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HUANG, XIANGJI. "COMPARISON OF INTERESTINGNESS MEASURES FOR WEB USAGE MINING: AN EMPIRICAL STUDY." International Journal of Information Technology & Decision Making 06, no. 01 (2007): 15–41. http://dx.doi.org/10.1142/s0219622007002368.

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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.

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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 (2017): 116. http://dx.doi.org/10.3390/rs9020116.

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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.

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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 (2017): 343–72. http://dx.doi.org/10.1142/s0218194017500127.

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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.

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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 (2017): 1750002. http://dx.doi.org/10.1142/s0218001417500021.

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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.

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Topçu, Emre. "Drought Monitoring Using MOWCATL Data Mining Algorithm in Aras Basin, Turkey." Earth Sciences Research Journal 26, no. 2 (2022): 183–96. http://dx.doi.org/10.15446/esrj.v26n2.94786.

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Drought is a natural phenomenon that occurs frequently and has some adverse effects on the ecosystem and humanity. Determination of drought beforehand is vital for optimal management of water resources. Many different methods have been developed to detect drought. Sequential association analysis is used for the data series analysis containing time information and is one of the methods used to determine the drought. A correlation can be established between the values taken by the data at different times when determining association rules with this method. The primary purpose of this study is to determine the sequential association patterns between precipitation and climate oscillation index for Aras Basin. The Aras basin is a region where irrigation and animal husbandry are common. Today, many dams and hydroelectric power plants, together with the increasing population, meet the water and energy needs. A possible drought event in this region will adversely affect the living things in the basin. Therefore, the study focused on this basin. Finding sequential associations between precipitation and climate oscillation index can determine the temporal correlations between these parameters and specifically detect drought. The MOWCATL (Minimal Occurrences with Constraints and Time Lags) algorithm was used to detect sequential associations, and the J-measure was used to evaluate the patterns in the study. Sequential association patterns were determined by applying this method to the precipitation data obtained from 6 meteorology stations in the Aras basin. AO (Arctic Oscillation) Index, MEI (Multivariate ENSO) Index, NAO (North Atlantic Oscillation) Index, Oceanic Niño Index (ONI), PDO (Pacific Decadal Oscillation) Index, PNA (Pacific/North American), and SOI (Southern Oscillation Index), followed by the 1, 3, 6 and 12-month Agricultural Standardized Precipitation Index (a-SPI) were used in sequential association. The study results revealed that the antecedent parameters were ineffective in detecting arid conditions in Ardahan and Doğubeyazıt stations, and they were influential on drought conditions, especially in a-SPI-3 and a-SPI-12 month periods at other stations. Although the altitude and geographical features are different, similar climatic patterns have been detected in some stations. As a result, it has been determined that climatic oscillations generally bring about typical situations in terms of drought for the Aras Basin.

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Dasgupta, Sarbani, and Banani Saha. "Study of Various Parallel Implementations of Association Rule Mining Algorithm." American Journal of Advanced Computing 1, no. 3 (2020): 1–7. http://dx.doi.org/10.15864/ajac.1305.

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In data mining, Apriori technique is generally used for frequent itemsets mining and association rule learning over transactional databases. The frequent itemsets generated by the Apriori technique provides association rules which are used for finding trends in the database. As the size of the database increases, sequential implementation of Apriori technique will take a lot of time and at one point of time the system may crash. To overcome this problem, several algorithms for parallel implementation of Apriori technique have been proposed. This paper gives a comparative study on various parallel implementation of Apriori technique .It also focuses on the advantages of using the Map Reduce technology, the latest technology used in parallelization of large dataset mining.

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Gakii, Consolata, Paul O. Mireji, and Richard Rimiru. "Graph Based Feature Selection for Reduction of Dimensionality in Next-Generation RNA Sequencing Datasets." Algorithms 15, no. 1 (2022): 21. http://dx.doi.org/10.3390/a15010021.

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Analysis of high-dimensional data, with more features (p) than observations (N) (p>N), places significant demand in cost and memory computational usage attributes. Feature selection can be used to reduce the dimensionality of the data. We used a graph-based approach, principal component analysis (PCA) and recursive feature elimination to select features for classification from RNAseq datasets from two lung cancer datasets. The selected features were discretized for association rule mining where support and lift were used to generate informative rules. Our results show that the graph-based feature selection improved the performance of sequential minimal optimization (SMO) and multilayer perceptron classifiers (MLP) in both datasets. In association rule mining, features selected using the graph-based approach outperformed the other two feature-selection techniques at a support of 0.5 and lift of 2. The non-redundant rules reflect the inherent relationships between features. Biological features are usually related to functions in living systems, a relationship that cannot be deduced by feature selection and classification alone. Therefore, the graph-based feature-selection approach combined with rule mining is a suitable way of selecting and finding associations between features in high-dimensional RNAseq data.

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Pol, Urmila. "Design and Development of Apriori Algorithm for Sequential to concurrent mining using MPI." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 10, no. 7 (2013): 1785–90. http://dx.doi.org/10.24297/ijct.v10i7.7026.

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Owing to the conception of big data and massive data processing there are increasing owes related to the temporal aspects of the data processing. In order to address these issues a continuous progression in data collection, storage technologies, designing and implementing large-scale parallel algorithm for Data mining is seen to be emerging in a rapid pace. In this regards, the Apriori algorithms have a great impact for finding frequent item sets using candidate generation. This paper presents highlights on parallel algorithm for mining association rules using MPI for passing message base in the Master-Slave based structural model.

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Malik, C. K. Mohammed. "Web Mining Using Improved Apriori Algorithm." International Academic Journal of Innovative Research 9, no. 1 (2022): 52–60. http://dx.doi.org/10.9756/iajir/v9i1/iajir0917.

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In this study, we will be concentrating on one of the more recent advancements in data mining, specifically mining online usage. The purpose of web use mining is to gain usable knowledge from the data that web servers keep about the actions of its visitors by mining the data that is stored on such servers. By using the association rule generation in the Web domain, the pages that are most frequently referenced together can be combined into a single server session. This is possible because of the interconnected nature of the Web. In association rule mining, a technique known as frequent set mining is one of the methods that may be used to discover regular patterns from a web log file. When it comes to mining the usage of the web, the term association rules refers to groups of web pages that are accessed together and have a support value that is higher than a given threshold. The support can be expressed as a proportion of total transactions that match a particular pattern. With the aid of the presence or absence of association rules, web designers are able to effectively reconstruct the websites they have created for their clients. In this research, we have introduced a method called Aprior for the purpose of extracting frequent patterns from online log files. The findings of the experiments that were carried out on data relating to peoples use of the website indicate that general sequential patterns or frequent item sets are more suitable for use in Web customization and recommender systems.

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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 (2020): 69–82. http://dx.doi.org/10.1108/jhtt-08-2017-0059.

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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.

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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 (2019): 1959016. http://dx.doi.org/10.1142/s021800141959016x.

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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.

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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 (2020): 243. http://dx.doi.org/10.22146/ijccs.58107.

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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.

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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 (2019): 64–79. http://dx.doi.org/10.2478/ijcss-2019-0004.

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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.

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Manurung, Oktaviani, and Penda Sudarto Hasugian. "Analisa Algoritma Apriori Untuk Peminjaman Buku Pada Perpustakaan SMA 1 Silima Pungga-Pungga Parongil." remik 4, no. 1 (2019): 154–60. http://dx.doi.org/10.33395/remik.v4i1.10445.

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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

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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.

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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.

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Han, Ju-Hee, Jae-Woong Yoon, Hwa-Jung Yook, et al. "Evaluation of Atopic Dermatitis and Cutaneous Infectious Disorders Using Sequential Pattern Mining: A Nationwide Population-Based Cohort Study." Journal of Clinical Medicine 11, no. 12 (2022): 3422. http://dx.doi.org/10.3390/jcm11123422.

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According to previous studies, the increased risk of cutaneous infectious disorders in patients with atopic dermatitis (AD) is related to impaired epidermal function, abnormal systemic immune function, and lower antimicrobial peptides. In this study, we analyzed the association between AD and cutaneous infectious disorders in the real world using sequential pattern mining (SPM). We analyzed National Health Insurance data from 2010–2013 using SPM to identify comorbid cutaneous infectious diseases and the onset durations of comorbidities. Patients with AD were at greater risk for molluscum contagiosum (adjusted odds ratio (aOR), 5.273), impetigo (aOR, 2.852), chickenpox (aOR, 2.251), otitis media (aOR, 1.748), eczema herpeticum (aOR, 1.292), and viral warts (aOR, 1.105). In SPM analysis, comorbidity of 1.06% shown in molluscum contagiosum was the highest value, and the duration of 77.42 days documented for molluscum contagiosum was the shortest onset duration among all the association rules. This study suggests that AD is associated with an increased risk of cutaneous infectious disorders. In particular, care should be taken regarding its high relevance with impetigo, molluscum contagiosum, and otitis media, which may help in preventing AD from worsening through appropriately preventing and managing the condition.

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K. AL-Taie, Rana Riad, Basma Jumaa Saleh, Ahmed Yousif Falih Saedi, and Lamees Abdalhasan Salman. "Analysis of WEKA data mining algorithms Bayes net, random forest, MLP and SMO for heart disease prediction system: A case study in Iraq." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 6 (2021): 5229. http://dx.doi.org/10.11591/ijece.v11i6.pp5229-5239.

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Data mining is defined as a search through large amounts of data for valuable information. The association rules, grouping, clustering, prediction, sequence modeling is some essential and most general strategies for data extraction. The processing of data plays a major role in the healthcare industry's disease detection. A variety of disease evaluations should be required to diagnose the patient. However, using data mining strategies, the number of examinations should be decreased. This decreased examination plays a crucial role in terms of time and results. Heart disease is a death-provoking disorder. In this recent instance, health issues are immense because of the availability of health issues and the grouping of various situations. Today, secret information is important in the healthcare industry to make decisions. For the prediction of cardiovascular problems, (Weka 3.8.3) tools for this analysis are used for the prediction of data extraction algorithms like sequential minimal optimization (SMO), multilayer perceptron (MLP), random forest and Bayes net. The data collected combine the prediction accuracy results, the receiver operating characteristic (ROC) curve, and the PRC value. The performance of Bayes net (94.5%) and random forest (94%) technologies indicates optimum performance rather than the sequential minimal optimization (SMO) and multilayer perceptron (MLP) methods.

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Kim, Jae Kyeong, Hyun Sil Moon, Byong Ju An, and Il Young Choi. "A grocery recommendation for off-line shoppers." Online Information Review 42, no. 4 (2018): 468–81. http://dx.doi.org/10.1108/oir-04-2016-0104.

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Purpose Many off-line retailers have experienced a slump in sales and have the potential risk of overstock or understock. To overcome these problems, retailers have applied data mining techniques, such as association rule mining or sequential association rule mining, to increase sales and predict product demand. However, because these techniques cannot generate shopper-centric rules, many off-line shoppers are often inconvenienced after writing their shopping lists carefully and comprehensively. Therefore, the purpose of this paper is to propose a personalized recommendation methodology for off-line grocery shoppers. Design/methodology/approach This paper employs a Markov chain model to generate recommendations for the shopper’s next shopping basket. The proposed methodology is based on the knowledge of both purchased products and purchase sequences. This paper compares the proposed methodology with a traditional collaborative filtering (CF)-based system, a bestseller-based system and a Markov-chain-based system as benchmark systems. Findings The proposed methodology achieves improvements of 15.87, 14.06 and 37.74 percent with respect to the CF-, Markov chain-, and best-seller-based benchmark systems, respectively, meaning that not only the purchased products but also the purchase sequences are important elements in the personalization of grocery recommendations. Originality/value Most of the previous studies on this topic have proposed on-line recommendation methodologies. However, because off-line stores collect transaction data from point-of-sale devices, this research proposes a methodology based on purchased products and purchase patterns for off-line grocery recommendations. In practice, this study implies that both purchased products and purchase sequences are viable elements in off-line grocery recommendations.

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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 (2006): 917–44. http://dx.doi.org/10.1142/s0218213006003041.

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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.

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Muhajir, Muhammad, and Berky Rian Efanna. "Association Rule Algorithm Sequential Pattern Discovery using Equivalent Classes (SPADE) to Analyze the Genesis Pattern of Landslides in Indonesia." International Journal of Advances in Intelligent Informatics 1, no. 3 (2015): 158. http://dx.doi.org/10.26555/ijain.v1i3.50.

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Landslide is one of movement of soil, rock, soil creep, and rock debris that occurred the move of the slopes. It is caused by steep slopes, high rainfall, deforestation, mining activities, and erosion. The impacts of the landslide are loss of property, damage to facilities such as homes and buildings, casualties, psychological trauma, disrupted economic and environmental damage. Based on the impacts of landslide, mitigation required to take early precautions are to know how the pattern of association between the sequence of events landslides and to know how the associative relationship pattern of earthquakes. Based on the impacts, the results of this research is associative relationship pattern is obtained from data flood that occurs in Indonesia, namely in case of heavy rain will occur labile soil structure to support the value of 0.37, confidence level of 41% and the power of formed ruled is 1.02.

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Chen, Yen-Liang, Shih-Sheng Chen, and Ping-Yu Hsu. "Mining hybrid sequential patterns and sequential rules." Information Systems 27, no. 5 (2002): 345–62. http://dx.doi.org/10.1016/s0306-4379(02)00008-x.

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Van, Trang, and Bac Le. "Mining sequential rules with itemset constraints." Applied Intelligence 51, no. 10 (2021): 7208–20. http://dx.doi.org/10.1007/s10489-020-02153-w.

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Lu, Songfeng, Heping Hu, and Fan Li. "Mining weighted association rules." Intelligent Data Analysis 5, no. 3 (2001): 211–25. http://dx.doi.org/10.3233/ida-2001-5303.

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Defit, Sarjon. "Intelligent Mining Association Rules." International Journal of Computer Science and Information Technology 4, no. 4 (2012): 97–106. http://dx.doi.org/10.5121/ijcsit.2012.4409.

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Srikant, Ramakrishnan, and Rakesh Agrawal. "Mining generalized association rules." Future Generation Computer Systems 13, no. 2-3 (1997): 161–80. http://dx.doi.org/10.1016/s0167-739x(97)00019-8.

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Mani, Tushar. "Mining Negative Association Rules." IOSR Journal of Computer Engineering 3, no. 6 (2012): 43–47. http://dx.doi.org/10.9790/0661-0364347.

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Chen, Jifan, and Muhammad Talha. "Audit Data Analysis and Application Based on Correlation Analysis Algorithm." Computational and Mathematical Methods in Medicine 2021 (November 15, 2021): 1–11. http://dx.doi.org/10.1155/2021/2059432.

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Traditional audit data analysis algorithms have many shortcomings, such as the lack of means to mine the hidden audit clues behind the data, the difficulty of finding increasingly hidden cheating techniques caused by the electronic and networked environment, and the inability to solve the quality defects of the audited data. Correlation analysis algorithm in data mining technology is an effective means to obtain knowledge from massive data, which can complete, muffle, clean, and reduce defective data and then can analyze massive data and obtain audit trails under the guidance of expert experience or analysts. Therefore, on the basis of summarizing and analyzing previous research works, this paper expounds the research status and significance of audit data analysis and application; elaborates the development background, current status, and future challenges of correlation analysis algorithm; introduces the methods and principles of data model and its conversion and audit model construction; conducts audit data collection and cleaning; implements audit data preprocessing and its algorithm description; performs audit data analysis based on correlation analysis algorithm; analyzes the hidden node activation value and audit rule extraction in correlation analysis algorithm; proposes the application of audit data based on correlation analysis algorithm; discusses the relationship between audit data quality and audit risk; and finally compares different data mining algorithms in audit data analysis. The findings demonstrate that by analyzing association rules, the correlation analysis algorithm can determine the significance of a huge quantity of audit data and characterise the degree to which linked events would occur concurrently or sequentially in a probabilistic manner. The correlation analysis algorithm first inputs the collected audit data through preprocessing module to filter out useless data and then organizes the obtained data into a format that can be recognized by data mining algorithm and executes the correlation analysis algorithm on the sorted data; finally, the obtained hidden data is divided into normal data and suspicious data by comparing it with the pattern in the rule base. The algorithm can conduct in-depth analysis and research on the company’s accounting vouchers, account books, and a large number of financial accounting data and other data of various natures in the company’s accounting vouchers; reveal its original characteristics and internal connections; and turn it into an audit. People need more direct and useful information. The study results of this paper provide a reference for further researches on audit data analysis and application based on correlation analysis algorithm.

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Tan, Jun, and Ying Yong Bu. "Association Rules Mining in Manufacturing." Applied Mechanics and Materials 34-35 (October 2010): 651–54. http://dx.doi.org/10.4028/www.scientific.net/amm.34-35.651.

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In recent years, manufacturing processes have become more and more complex, manufacturing activities generate large quantities of data, so it is no longer practical to rely on traditional manual methods to analyze this data. Data mining offers tools for extracting knowledge from data, leading to significant improvement in the decision-making process. Association rules mining is one of the most important data mining techniques and has received considerable attention from researchers and practitioners. The paper presents the basic concept of association rule mining and reviews applications of association rules in manufacturing, including product design, manufacturing, process, customer relationship management, supply chain management, and product quality improvement. This paper is focused on demonstrating the relevancy of association rules mining to manufacturing industry, rather than discussing the association rules mining domain in general.

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Taha, Mohamed, Tarek F. Gharib, and Hamed Nassar. "DARM: Decremental Association Rules Mining." Journal of Intelligent Learning Systems and Applications 03, no. 03 (2011): 181–89. http://dx.doi.org/10.4236/jilsa.2011.33019.

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Liu, Fang, Zhengding Lu, and Songfeng Lu. "Mining association rules using clustering." Intelligent Data Analysis 5, no. 4 (2001): 309–26. http://dx.doi.org/10.3233/ida-2001-5403.

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Agrawal, R., and J. C. Shafer. "Parallel mining of association rules." IEEE Transactions on Knowledge and Data Engineering 8, no. 6 (1996): 962–69. http://dx.doi.org/10.1109/69.553164.

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Zaki, Mohammed J. "Mining Non-Redundant Association Rules." Data Mining and Knowledge Discovery 9, no. 3 (2004): 223–48. http://dx.doi.org/10.1023/b:dami.0000040429.96086.c7.

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Nanopoulos, Alexandros, and Yannis Manolopoulos. "Memory-adaptive association rules mining." Information Systems 29, no. 5 (2004): 365–84. http://dx.doi.org/10.1016/s0306-4379(03)00035-8.

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Chiang, Ding-An, Yi-Fan Wang, Yi-Hsin Wang, Zhi-Yang Chen, and Mei-Hua Hsu. "Mining disjunctive consequent association rules." Applied Soft Computing 11, no. 2 (2011): 2129–33. http://dx.doi.org/10.1016/j.asoc.2010.07.011.

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Taniar, David, Wenny Rahayu, Olena Daly, and Hong-Quang Nguyen. "Mining Hierarchical Negative Association Rules." International Journal of Computational Intelligence Systems 5, no. 3 (2012): 434–51. http://dx.doi.org/10.1080/18756891.2012.696905.

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Subramanyam, R. B. V., and A. Goswami. "Mining fuzzy quantitative association rules." Expert Systems 23, no. 4 (2006): 212–25. http://dx.doi.org/10.1111/j.1468-0394.2006.00402.x.

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Lee, Wan-Jui, Jung-Yi Jiang, and Shie-Jue Lee. "Mining fuzzy periodic association rules." Data & Knowledge Engineering 65, no. 3 (2008): 442–62. http://dx.doi.org/10.1016/j.datak.2007.11.002.

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Abdelwahab, Amira, and Nesma Youssef. "Performance Evaluation of Sequential Rule Mining Algorithms." Applied Sciences 12, no. 10 (2022): 5230. http://dx.doi.org/10.3390/app12105230.

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Data mining techniques are useful in discovering hidden knowledge from large databases. One of its common techniques is sequential rule mining. A sequential rule (SR) helps in finding all sequential rules that achieved support and confidence threshold for help in prediction. It is an alternative to sequential pattern mining in that it takes the probability of the following patterns into account. In this paper, we address the preferable utilization of sequential rule mining algorithms by applying them to databases with different features for improving the efficiency in different fields of application. The three compared algorithms are the TRuleGrowth algorithm, which is an extension sequential rule algorithm of RuleGrowth; the top-k non-redundant sequential rules algorithm (TNS); and a non-redundant dynamic bit vector (NRD-DBV). The analysis compares the three algorithms regarding the run time, the number of produced rules, and the used memory to nominate which of them is best suited in prediction. Additionally, it explores the most suitable applications for each algorithm to improve the efficiency. The experimental results proved that the performance of the algorithms appears related to the dataset characteristics. It has been demonstrated that altering the window size constraint, determining the number of created rules, or changing the value of the minSup threshold can reduce execution time and control the number of valid rules generated.

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Do Van, Thanh, and Phuong Truong Duc. "FUZZY COMMON SEQUENTIAL RULES MINING IN QUANTITATIVE SEQUENCE DATABASES." Journal of Computer Science and Cybernetics 35, no. 3 (2019): 217–32. http://dx.doi.org/10.15625/1813-9663/0/0/13277.

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Common Sequential Rules present a relationship between unordered itemsets in which the items in antecedents have to appear before ones in consequents. The algorithms proposed to find the such rules so far are only applied for transactional sequence databases, not applied for quantitative sequence databases.The goal of this paper is to propose a new algorithm for finding the fuzzy common sequential (FCS for short) rules in quantitative sequence databases. The proposed algorithm is improved by basing on the ERMiner algorithm. It is considered to be the most effective today compared to other algorithms for finding common sequential rules in transactional sequence database. FCS rules are more general than classical fuzzy sequential rules and are useful in marketing, market analysis, medical diagnosis and treatment

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Do Van, Thanh, and Phuong Truong Duc. "FUZZY COMMON SEQUENTIAL RULES MINING IN QUANTITATIVE SEQUENCE DATABASES." Journal of Computer Science and Cybernetics 35, no. 3 (2019): 217–32. http://dx.doi.org/10.15625/1813-9663/35/3/13277.

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Abstract:

Common Sequential Rules present a relationship between unordered itemsets in which the items in antecedents have to appear before ones in consequents. The algorithms proposed to find the such rules so far are only applied for transactional sequence databases, not applied for quantitative sequence databases.The goal of this paper is to propose a new algorithm for finding the fuzzy common sequential (FCS for short) rules in quantitative sequence databases. The proposed algorithm is improved by basing on the ERMiner algorithm. It is considered to be the most effective today compared to other algorithms for finding common sequential rules in transactional sequence database. FCS rules are more general than classical fuzzy sequential rules and are useful in marketing, market analysis, medical diagnosis and treatment

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Thanh, Do Van, and Truong Duc Phuong. "Mining Fuzzy Common Sequential Rules with Fuzzy Time-Interval in Quantitative Sequence Databases." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 28, no. 06 (2020): 957–79. http://dx.doi.org/10.1142/s0218488520500427.

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There are two kinds of sequential rules. They are classical sequential rules and common sequential rules. The common sequential rules present the relationship between unordered itemsets in which all the items in the antecedent part have to appear before the ones in the consequent part. All existing algorithms for mining common sequential rules can not apply to quantitative sequence databases. Furthermore, the common sequential rules found so far did not yet reveal the time gap about the apperance of itemsets in its antecedent and consequent parts. The purpose of this article is to overcome the two disadvantages mentioned above. Specifically, the article proposes an algorithm called IFERMiner to discover common sequential rules in quantitative sequence databases, where the time gap about appearance of two attribute sets in its antecedent and consequent parts is taken account. This algorithm was developed from the ERMiner algorithm that is the most efficient algorithm to discover common sequential rules in transactional sequence databases now. The computational complexity of the IFERMiner algorithm is also shown in the article and it is polynomial. The FCSI rules found out by the IFERMiner algorithm are useful for marketing domain and market analysis.

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Fournier-Viger, Philippe, Usef Faghihi, Roger Nkambou, and Engelbert Mephu Nguifo. "CMRules: Mining sequential rules common to several sequences." Knowledge-Based Systems 25, no. 1 (2012): 63–76. http://dx.doi.org/10.1016/j.knosys.2011.07.005.

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U., Deepa, and Nilam K. "Mining Association Rules using R Environment." International Journal of Computer Applications 157, no. 4 (2017): 45–50. http://dx.doi.org/10.5120/ijca2017912679.

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Pandey, Sachin. "Multilevel Association Rules in Data Mining." Journal of Advances and Scholarly Researches in Allied Education 15, no. 5 (2018): 74–78. http://dx.doi.org/10.29070/15/57517.

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Tjioe, Haorianto Cokrowijoyo, and David Taniar. "Mining Association Rules in Data Warehouses." International Journal of Data Warehousing and Mining 1, no. 3 (2005): 28–62. http://dx.doi.org/10.4018/jdwm.2005070103.

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Tan, Jun. "Weighted Association Rules Mining Algorithm Research." Applied Mechanics and Materials 241-244 (December 2012): 1598–601. http://dx.doi.org/10.4028/www.scientific.net/amm.241-244.1598.

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Aiming at the problem that most of weighted association rules mining algorithms have not the anti-monotonicity, this paper presents a weighted support-confidence framework which supports anti-monotonicity. On this basis, weighted boolean association rules mining algorithm and weighted fuzzy association rules mining algorithm are presented, which use pruning strategy of Apriori algorithm so that improve the efficiency of frequent itemsets generated. Experimental results show that both algorithms have good performance.

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Hong, Tzung-Pei, Chan-Sheng Kuo, and Sheng-Chai Chi. "Mining association rules from quantitative data☆." Intelligent Data Analysis 3, no. 5 (1999): 363–76. http://dx.doi.org/10.3233/ida-1999-3504.

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Huang, Yin-Fu, and Chieh-Ming Wu. "Preknowledge-based generalized association rules mining." Journal of Intelligent & Fuzzy Systems 22, no. 1 (2011): 1–13. http://dx.doi.org/10.3233/ifs-2010-0469.

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Dong, Liyan, Renbiao Wang, and Yongli Li. "Mining Association Rules Based on Certainty." International Journal of Intelligent Engineering and Systems 5, no. 3 (2012): 19–27. http://dx.doi.org/10.22266/ijies2012.9030.03.

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