Relevant bibliographies by topics / Sequential rules and patterns

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Sequential rules and patterns'

Author: Grafiati
Published: 4 June 2021
Last updated: 10 February 2022

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Journal articles on the topic "Sequential rules and patterns"

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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 (July 2002): 345–62. http://dx.doi.org/10.1016/s0306-4379(02)00008-x.

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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.

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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.

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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.
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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 (July 31, 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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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.

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<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>
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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.

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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 (March 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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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.

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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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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.

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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.
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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 (February 26, 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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Dissertations / Theses on the topic "Sequential rules and patterns"

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Wu, Sheng-Tang. "Knowledge discovery using pattern taxonomy model in text mining." Queensland University of Technology, 2007. http://eprints.qut.edu.au/16675/.

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In the last decade, many data mining techniques have been proposed for fulfilling various knowledge discovery tasks in order to achieve the goal of retrieving useful information for users. Various types of patterns can then be generated using these techniques, such as sequential patterns, frequent itemsets, and closed and maximum patterns. However, how to effectively exploit the discovered patterns is still an open research issue, especially in the domain of text mining. Most of the text mining methods adopt the keyword-based approach to construct text representations which consist of single words or single terms, whereas other methods have tried to use phrases instead of keywords, based on the hypothesis that the information carried by a phrase is considered more than that by a single term. Nevertheless, these phrase-based methods did not yield significant improvements due to the fact that the patterns with high frequency (normally the shorter patterns) usually have a high value on exhaustivity but a low value on specificity, and thus the specific patterns encounter the low frequency problem. This thesis presents the research on the concept of developing an effective Pattern Taxonomy Model (PTM) to overcome the aforementioned problem by deploying discovered patterns into a hypothesis space. PTM is a pattern-based method which adopts the technique of sequential pattern mining and uses closed patterns as features in the representative. A PTM-based information filtering system is implemented and evaluated by a series of experiments on the latest version of the Reuters dataset, RCV1. The pattern evolution schemes are also proposed in this thesis with the attempt of utilising information from negative training examples to update the discovered knowledge. The results show that the PTM outperforms not only all up-to-date data mining-based methods, but also the traditional Rocchio and the state-of-the-art BM25 and Support Vector Machines (SVM) approaches.
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Andrade, Rodrigo Bomfim de. "Sequential cost-reimbursement rules." reponame:Repositório Institucional do FGV, 2014. http://hdl.handle.net/10438/11736.

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This paper studies cost-sharing rules under dynamic adverse selection. We present a typical principal-agent model with two periods, set up in Laffont and Tirole's (1986) canonical regulation environment. At first, when the contract is signed, the firm has prior uncertainty about its efficiency parameter. In the second period, the firm learns its efficiency and chooses the level of cost-reducing effort. The optimal mechanism sequentially screens the firm's types and achieves a higher level of welfare than its static counterpart. The contract is indirectly implemented by a sequence of transfers, consisting of a fixed advance payment based on the reported cost estimate, and an ex-post compensation linear in cost performance.
Este trabalho estuda regras de compartilhamento de custos sob seleção adversa dinâmica. Apresentamos um modelo típico de agente-principal com dois períodos, fundamentado no ambiente canônico de regulação de Laffont e Tirole (1986). De início, quando da assinatura do contrato, a firma possui incerteza prévia sobre seu parâmetro de eficiência. No segundo período, a firma aprende a sua eficiência e escolhe o nível de esforço para reduzir custos. O mecanismo ótimo efetua screening sequencial entre os tipos da firma e atinge um nível de bem-estar superior ao alcançado pelo mecanismo estático. O contrato é implementado indiretamente por uma sequência de transferências, que consiste em um pagamento fixo antecipado, baseado na estimativa de custos reportada pela firma, e uma compensação posterior linear no custo realizado.
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João, Rafael Stoffalette. "Mineração de padrões sequenciais e geração de regras de associação envolvendo temporalidade." Universidade Federal de São Carlos, 2015. https://repositorio.ufscar.br/handle/ufscar/8923.

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
Data mining aims at extracting useful information from a Database (DB). The mining process enables, also, to analyze the data (e.g. correlations, predictions, chronological relationships, etc.). The work described in this document proposes an approach to deal with temporal knowledge extraction from a DB and describes the implementation of this approach, as the computational system called S_MEMIS+AR. The system focuses on the process of finding frequent temporal patterns in a DB and generating temporal association rules, based on the elements contained in the frequent patterns identified. At the end of the process performs an analysis of the temporal relationships between time intervals associated with the elements contained in each pattern using the binary relationships described by the Allen´s Interval Algebra. Both, the S_MEMISP+AR and the algorithm that the system implements, were subsidized by the Apriori, the MEMISP and the ARMADA approaches. Three experiments considering two different approaches were conducted with the S_MEMISP+AR, using a DB of sale records of products available in a supermarket. Such experiments were conducted to show that each proposed approach, besides inferring new knowledge about the data domain and corroborating results that reinforce the implicit knowledge about the data, also promotes, in a global way, the refinement and extension of the knowledge about the data.
A mineração de dados tem como objetivo principal a extração de informações úteis a partir de uma Base de Dados (BD). O processo de mineração viabiliza, também, a realização de análises dos dados (e.g, identificação de correlações, predições, relações cronológicas, etc.). No trabalho descrito nesta dissertação é proposta uma abordagem à extração de conhecimento temporal a partir de uma BD e detalha a implementação dessa abordagem por meio de um sistema computacional chamado S_MEMISP+AR. De maneira simplista, o sistema tem como principal tarefa realizar uma busca por padrões temporais em uma base de dados, com o objetivo de gerar regras de associação temporais entre elementos de padrões identificados. Ao final do processo, uma análise das relações temporais entre os intervalos de duração dos elementos que compõem os padrões é feita, com base nas relações binárias descritas pelo formalismo da Álgebra Intervalar de Allen. O sistema computacional S_MEMISP+AR e o algoritmo que o sistema implementa são subsidiados pelas propostas Apriori, ARMADA e MEMISP. Foram realizados três experimentos distintos, adotando duas abordagens diferentes de uso do S_MEMISP+AR, utilizando uma base de dados contendo registros de venda de produtos disponibilizados em um supermercado. Tais experimentos foram apresentados como forma de evidenciar que cada uma das abordagens, além de inferir novo conhecimento sobre o domínio de dados e corroborar resultados que reforçam o conhecimento implícito já existente sobre os dados, promovem, de maneira global, o refinamento e extensão do conhecimento sobre os dados.
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Abar, Orhan. "Rule Mining and Sequential Pattern Based Predictive Modeling with EMR Data." UKnowledge, 2019. https://uknowledge.uky.edu/cs_etds/85.

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Electronic medical record (EMR) data is collected on a daily basis at hospitals and other healthcare facilities to track patients’ health situations including conditions, treatments (medications, procedures), diagnostics (labs) and associated healthcare operations. Besides being useful for individual patient care and hospital operations (e.g., billing, triaging), EMRs can also be exploited for secondary data analyses to glean discriminative patterns that hold across patient cohorts for different phenotypes. These patterns in turn can yield high level insights into disease progression with interventional potential. In this dissertation, using a large scale realistic EMR dataset of over one million patients visiting University of Kentucky healthcare facilities, we explore data mining and machine learning methods for association rule (AR) mining and predictive modeling with mood and anxiety disorders as use-cases. Our first work involves analysis of existing quantitative measures of rule interestingness to assess how they align with a practicing psychiatrist’s sense of novelty/surprise corresponding to ARs identified from EMRs. Our second effort involves mining causal ARs with depression and anxiety disorders as target conditions through matching methods accounting for computationally identified confounding attributes. Our final effort involves efficient implementation (via GPUs) and application of contrast pattern mining to predictive modeling for mental conditions using various representational methods and recurrent neural networks. Overall, we demonstrate the effectiveness of rule mining methods in secondary analyses of EMR data for identifying causal associations and building predictive models for diseases.
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Lu, Jing. "From sequential patterns to concurrent branch patterns : a new post sequential patterns mining approach." Thesis, University of Bedfordshire, 2006. http://hdl.handle.net/10547/556399.

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Sequential patterns mining is an important pattern discovery technique used to identify frequently observed sequential occurrence of items across ordered transactions over time. It has been intensively studied and there exists a great diversity of algorithms. However, there is a major problem associated with the conventional sequential patterns mining in that patterns derived are often large and not very easy to understand or use. In addition, more complex relations among events are often hidden behind sequences. A novel model for sequential patterns called Sequential Patterns Graph (SPG) is proposed. The construction algorithm of SPG is presented with experimental results to substantiate the concept. The thesis then sets out to define some new structural patterns such as concurrent branch patterns, exclusive patterns and iterative patterns which are generally hidden behind sequential patterns. Finally, an integrative framework, named Post Sequential Patterns Mining (PSPM), which is based on sequential patterns mining, is also proposed for the discovery and visualisation of structural patterns. This thesis is intended to prove that discrete sequential patterns derived from traditional sequential patterns mining can be modelled graphically using SPG. It is concluded from experiments and theoretical studies that SPG is not only a minimal representation of sequential patterns mining, but it also represents the interrelation among patterns and establishes further the foundation for mining structural knowledge (i.e. concurrent branch patterns, exclusive patterns and iterative patterns). from experiments conducted on both synthetic and real datasets, it is shown that Concurrent Branch Patterns (CBP) mining is an effective and efficient mining algorithm suitable for concurrent branch patterns.
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Mooney, Carl Howard, and carl mooney@bigpond com. "The Discovery of Interacting Episodes and Temporal Rule Determination in Sequential Pattern Mining." Flinders University. Informatics and Engineering, 2007. http://catalogue.flinders.edu.au./local/adt/public/adt-SFU20070702.120306.

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The reason for data mining is to generate rules that can be used as the basis for making decisions. One such area is sequence mining which, in terms of transactional datasets, can be stated as the discovery of inter-transaction associations or associations between different transactions. The data used for sequence mining is not limited to data stored in overtly temporal or longitudinally maintained datasets and in such domains data can be viewed as a series of events, or episodes, occurring at specific times. The problem thus becomes a search for collections of events that occur frequently together. While the mining of frequent episodes is an important capability, the manner in which such episodes interact can provide further useful knowledge in the search for a description of the behaviour of a phenomenon but as yet has received little investigation. Moreover, while many sequences are associated with absolute time values, most sequence mining routines treat time in a relative sense, returning only patterns that can be described in terms of Allen-style relationships (or simpler), ie. nothing about the relative pace of occurrence. They thus produce rules with a more limited expressive power. Up to this point in time temporal interval patterns have been based on the endpoints of the intervals, however in many cases the ‘natural’ point of reference is the midpoint of an interval and it is therefore appropriate to develop a mechanism for reasoning between intervals when midpoint information is known. This thesis presents a method for discovering interacting episodes from temporal sequences and the analysis of them using temporal patterns. The mining can be conducted both with and without the mechanism for handling the pace of events and the analysis is conducted using both the traditional interval algebras and a midpoint algebra presented in this thesis. The visualisation of rules in data mining is a large and dynamic field in its own right and although there has been a great deal of research in the visualisation of associations, there has been little in the area of sequence or episodic mining. Add to this the emerging field of mining stream data and there is a need to pursue methods and structures for such visualisations, and as such this thesis also contributes toward research in this important area of visualisation.
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Muzammal, Muhammad. "Mining sequential patterns from probabilistic data." Thesis, University of Leicester, 2012. http://hdl.handle.net/2381/27638.

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Sequential Pattern Mining (SPM) is an important data mining problem. Although it is assumed in classical SPM that the data to be mined is deterministic, it is now recognized that data obtained from a wide variety of data sources is inherently noisy or uncertain, such as data from sensors or data being collected from the web from different (potentially conflicting) data sources. Probabilistic databases is a popular framework for modelling uncertainty. Recently, several data mining and ranking problems have been studied in probabilistic databases. To the best of our knowledge, this is the first systematic study of mining sequential patterns from probabilistic databases. In this work, we consider the kind of uncertainties that could arise in SPM. We propose four novel uncertainty models for SPM, namely tuple-level uncertainty, event-level uncertainty, source-level uncertainty and source-level uncertainty in deduplication, all of which fit into the probabilistic databases framework, and motivate them using potential real-life scenarios. We then define the interestingness predicate for two measures of interestingness, namely expected support and probabilistic frequentness. Next, we consider the computational complexity of evaluating the interestingness predicate, for various combinations of uncertainty models and interestingness measures, and show that different combinations have very different outcomes from a complexity theoretic viewpoint: whilst some cases are computationally tractable, we show other cases to be computationally intractable. We give a dynamic programming algorithm to compute the source support probability and hence the expected support of a sequence in a source-level uncertain database. We then propose optimizations to speedup the support computation task. Next, we propose probabilistic SPM algorithms based on the candidate generation and pattern growth frameworks for the source-level uncertainty model and the expected support measure. We implement these algorithms and give an empirical evaluation of the probabilistic SPM algorithms and show the scalability of these algorithms under different parameter settings using both real and synthetic datasets. Finally, we demonstrate the effectiveness of the probabilistic SPM framework at extracting meaningful patterns in the presence of noise.
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Samamé, Jimenez Hilda Ana. "Recommender systems using temporal restricted sequential patterns." Master's thesis, Pontificia Universidad Católica del Perú, 2021. http://hdl.handle.net/20.500.12404/18784.

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Recommendation systems are algorithms for suggesting relevant items to users. Generally, the recommendations are expressed in what will be recommended and a value representing the recommendation's relevance. However, forecasting if the user will buy the recommended item in the next day, week, or month is crucial for companies. The present study describes a process to suggest items from sequential patterns under temporal restrictions.
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Brown, Shawn Paul. "Rules and patterns of microbial community assembly." Diss., Kansas State University, 2014. http://hdl.handle.net/2097/18324.

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Doctor of Philosophy
Division of Biology
Ari M. Jumpponen
Microorganisms are critically important for establishing and maintaining ecosystem properties and processes that fuel and sustain higher-trophic levels. Despite the universal importance of microbes, we know relatively little about the rules and processes that dictate how microbial communities establish and assemble. Largely, we rely on assumptions that microbial community establishment follow similar trajectories as plants, but on a smaller scale. However, these assumptions have been rarely validated and when validation has been attempted, the plant-based theoretical models apply poorly to microbial communities. Here, I utilized genomics-inspired tools to interrogate microbial communities at levels near community saturation to elucidate the rules and patterns of microbial community assembly. I relied on a community filtering model as a framework: potential members of the microbial community are filtered through environmental and/or biotic filters that control which taxa can establish, persist, and coexist. Additionally, I addressed whether two different microbial groups (fungi and bacteria) share similar assembly patterns. Similar dispersal capabilities and mechanisms are thought to result in similar community assembly rules for fungi and bacteria. I queried fungal and bacterial communities along a deglaciated primary successional chronosequence to determine microbial successional dynamics and to determine if fungal and bacterial assemblies are similar or follow trajectories similar to plants. These experiments demonstrate that not only do microbial community assembly dynamics not follow plant-based models of succession, but also that fungal and bacterial community assembly dynamics are distinct. We can no longer assume that because fungi and bacteria share small propagule sizes they follow similar trends. Further, additional studies targeting biotic filters (here, snow algae) suggest strong controls during community assembly, possibly because of fungal predation of the algae or because of fungal utilization of algal exudates. Finally, I examined various technical aspects of sequence-based ecological investigations. These studies aimed to improve microbial community data reliability and analyses.
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Yang, Can. "Discovering Contiguous Sequential Patterns in Network-Constrained Movement." Licentiate thesis, KTH, Geoinformatik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-217998.

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A large proportion of movement in urban area is constrained to a road network such as pedestrian, bicycle and vehicle. That movement information is commonly collected by Global Positioning System (GPS) sensor, which has generated large collections of trajectories. A contiguous sequential pattern (CSP) in these trajectories represents a certain number of objects traversing a sequence of spatially contiguous edges in the network, which is an intuitive way to study regularities in network-constrained movement. CSPs are closely related to route choices and traffic flows and can be useful in travel demand modeling and transportation planning. However, the efficient and scalable extraction of CSPs and effective visualization of the heavily overlapping CSPs are remaining challenges. To address these challenges, the thesis develops two algorithms and a visual analytics system. Firstly, a fast map matching (FMM) algorithm is designed for matching a noisy trajectory to a sequence of edges traversed by the object with a high performance. Secondly, an algorithm called bidirectional pruning based closed contiguous sequential pattern mining (BP-CCSM) is developed to extract sequential patterns with closeness and contiguity constraint from the map matched trajectories. Finally, a visual analytics system called sequential pattern explorer for trajectories (SPET) is designed for interactive mining and visualization of CSPs in a large collection of trajectories. Extensive experiments are performed on a real-world taxi trip GPS dataset to evaluate the algorithms and visual analytics system. The results demonstrate that FMM achieves a superior performance by replacing repeated routing queries with hash table lookups. BP-CCSM considerably outperforms three state-of-the-art algorithms in terms of running time and memory consumption. SPET enables the user to efficiently and conveniently explore spatial and temporal variations of CSPs in network-constrained movement.

QC 20171122

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Books on the topic "Sequential rules and patterns"

1

Adamo, Jean-Marc. Data Mining for Association Rules and Sequential Patterns. New York, NY: Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4613-0085-4.

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Data Mining for Association Rules and Sequential Patterns: Sequential and Parallel Algorithms. New York, NY: Springer New York, 2001.

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Shiri︠a︡ev, Alʹbert Nikolaevich. Optimal stopping rules. Berlin: Springer, 2008.

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K, Kokula Krishna Hari, ed. Entity Mining Extraction Using Sequential Rules: ICIEMS 2014. India: Association of Scientists, Developers and Faculties, 2014.

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Yang, Y. X. Extracting boolean rules from CA patterns. Sheffield: University of Sheffield, Dept. of Automatic Control and Systems Engineering, 1998.

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Günthner, Susanne, Wolfgang Imo, and Jörg Bücker. Grammar and dialogism: Sequential, syntactic, and prosodic patterns between emergence and sedimentation. Berlin: De Gruyter Mouton, 2014.

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Adventures in knitting: Breaking the rules and creating unique designs. London: Blandford, 1991.

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Albala-Bertrand, J. M. Natural disasters in Latin America: Economic patterns and performance rules. London: Queen Mary and Westfield College, Dept. of Economics, 1992.

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Willis, David. Rules, patterns and words: Grammar and lexis in English language teaching. Cambridge: Cambridge University Press, 2003.

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Froot, Kenneth. Interest allocation rules, financing patterns, and the operations of U.S. multinationals. Cambridge, MA: National Bureau of Economic Research, 1994.

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Book chapters on the topic "Sequential rules and patterns"

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Liu, Bing. "Association Rules and Sequential Patterns." In Web Data Mining, 17–62. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19460-3_2.

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Adamo, Jean-Marc. "Optimizing Rules with Quantitative Attributes." In Data Mining for Association Rules and Sequential Patterns, 111–50. New York, NY: Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4613-0085-4_8.

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Adamo, Jean-Marc. "Mining for Rules over Attribute Taxonomies." In Data Mining for Association Rules and Sequential Patterns, 49–65. New York, NY: Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4613-0085-4_4.

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Adamo, Jean-Marc. "Search Space Partition-Based Sequential Pattern Mining." In Data Mining for Association Rules and Sequential Patterns, 185–228. New York, NY: Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4613-0085-4_10.

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Adamo, Jean-Marc. "Mining for Rules with Categorical and Metric Attributes." In Data Mining for Association Rules and Sequential Patterns, 93–109. New York, NY: Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4613-0085-4_7.

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Adamo, Jean-Marc. "Introduction." In Data Mining for Association Rules and Sequential Patterns, 1–4. New York, NY: Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4613-0085-4_1.

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Adamo, Jean-Marc. "Search Space Partition-Based Rule Mining." In Data Mining for Association Rules and Sequential Patterns, 5–32. New York, NY: Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4613-0085-4_2.

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Adamo, Jean-Marc. "Apriori and Other Algorithms." In Data Mining for Association Rules and Sequential Patterns, 33–48. New York, NY: Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4613-0085-4_3.

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Adamo, Jean-Marc. "Constraint-Based Rule Mining." In Data Mining for Association Rules and Sequential Patterns, 67–78. New York, NY: Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4613-0085-4_5.

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Adamo, Jean-Marc. "Data Partition-Based Rule Mining." In Data Mining for Association Rules and Sequential Patterns, 79–91. New York, NY: Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4613-0085-4_6.

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Conference papers on the topic "Sequential rules and patterns"

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Tang, Yeming, Qiuli Tong, and Zhao Du. "Mining frequent sequential patterns and association rules on campus map system." In 2014 2nd International Conference on Systems and Informatics (ICSAI). IEEE, 2014. http://dx.doi.org/10.1109/icsai.2014.7009423.

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Hu, Ya-Han, Yen-Liang Chen, and Er-Hsuan Lin. "Classification of Time-Sequential Attributes by Using Sequential Pattern Rules." In Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007). IEEE, 2007. http://dx.doi.org/10.1109/fskd.2007.217.

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Hou, Sizu, and Xianfei Zhang. "Alarms Association Rules Based on Sequential Pattern Mining Algorithm." In 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). IEEE, 2008. http://dx.doi.org/10.1109/fskd.2008.11.

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Izza, Yacine, Said Jabbour, Badran Raddaoui, and Abdelahmid Boudane. "On the Enumeration of Association Rules: A Decomposition-based Approach." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/176.

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While traditional data mining techniques have been used extensively for finding patterns in databases, they are not always suitable for incorporating user-specified constraints. To overcome this issue, CP and SAT based frameworks for modeling and solving pattern mining tasks have gained a considerable audience in recent years. However, a bottleneck for all these CP and SAT-based approaches is the encoding size which makes these algorithms inefficient for large databases. This paper introduces a practical SAT-based approach to discover efficiently (minimal non-redundant) association rules. First, we present a decomposition-based paradigm that splits the original transaction database into smaller and independent subsets. Then, we show that without producing too large formulas, our decomposition method allows independent mining evaluation on a multi-core machine, improving performance. Finally, an experimental evaluation shows that our method is fast and scale well compared with the existing CP approach even in the sequential case, while significantly reducing the gap with the best state-of-the-art specialized algorithm.
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Wang, Chong, Jian Liu, and Yanqing Wang. "Mining e-shoppers' purchase rules based on k-trees sequential pattern." In 2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). IEEE, 2010. http://dx.doi.org/10.1109/fskd.2010.5569203.

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Noughabi, Elham Akhond Zadeh, Amir Albadvi, and Behrouz Homayoun Far. "How Can We Explore Patterns of Customer Segments' Structural Changes? A Sequential Rule Mining Approach." In 2015 IEEE International Conference on Information Reuse and Integration (IRI). IEEE, 2015. http://dx.doi.org/10.1109/iri.2015.52.

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Wang, Xilu, and Weili Yao. "Sequential Pattern Mining: Optimum Maximum Sequential Patterns and Consistent Sequential Patterns." In 2007 IEEE International Conference on Integration Technology. IEEE, 2007. http://dx.doi.org/10.1109/icitechnology.2007.4290497.

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Singham, Dashi I., and Lee W. Schruben. "Analysis of sequential stopping rules." In 2009 Winter Simulation Conference - (WSC 2009). IEEE, 2009. http://dx.doi.org/10.1109/wsc.2009.5429686.

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Huang, Xiao-hong, and Xiu-feng Zhang. "Mining multi-attribute event sequential pattern based on association rule." In 2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). IEEE, 2010. http://dx.doi.org/10.1109/fskd.2010.5569718.

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Raïssi, Chedy, and Jian Pei. "Towards bounding sequential patterns." In the 17th ACM SIGKDD international conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2020408.2020612.

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Reports on the topic "Sequential rules and patterns"

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Katehakis, Michael N., and Cyrus Derman. Computing Optimal Sequential Allocation Rules in Clinical Trials. Fort Belvoir, VA: Defense Technical Information Center, September 1985. http://dx.doi.org/10.21236/ada170801.

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Froot, Kenneth, and James Hines. Interest Allocation Rules, Financing Patterns, and the Operations of U.S. Multinationals. Cambridge, MA: National Bureau of Economic Research, November 1994. http://dx.doi.org/10.3386/w4924.

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Seno, Masakazu, and George Karypis. SLPMiner: An Algorithm for Finding Frequent Sequential Patterns Using Length-Decreasing Support Constraint. Fort Belvoir, VA: Defense Technical Information Center, June 2002. http://dx.doi.org/10.21236/ada438931.

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Hernández, Ana, Magaly Lavadenz, and JESSEA YOUNG. Mapping Writing Development in Young Bilingual Learners. CEEL, 2012. http://dx.doi.org/10.15365/ceel.article.2012.2.

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A growing interest in Two-Way Bilingual Immersion (TWBI) programs has led to increased attention to bilingualism, biliteracy, and biculturalism. This article describes the writing development in Spanish and English for 49 kindergarten students in a 50/50 Two-Way Bilingual Immersion program. Over the course of an academic year, the authors collected writing samples to analyze evidence of cross-linguistic resource sharing using a grounded theoretical approach to compare and contrast writing samples to determine patterns of cross-linguistic resource sharing in English and Spanish. The authors identified four patterns: phonological, syntactic, lexical, and metalinguistic awareness. Findings indicated that emergent writers applied similar strategies as older bilingual students, including lexical level code-switching, applied phonological rules of L1 to their respective L2s, and used experiential and content knowledge to write in their second language. These findings have instructional implications for both English Learners and native English speakers as well as for learning from students for program improvement.
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