Relevant bibliographies by topics / Sequential Association Rules Mining

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Academic literature on the topic 'Sequential Association Rules Mining'

Author: Grafiati
Published: 11 December 2022
Last updated: 26 January 2023

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Journal articles on the topic "Sequential Association Rules Mining"

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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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Dissertations / Theses on the topic "Sequential Association Rules Mining"

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Zang, Hao. "Non-redundant sequential association rule mining based on closed sequential patterns." Thesis, Queensland University of Technology, 2010. https://eprints.qut.edu.au/46166/1/Hao_Zang_Thesis.pdf.

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In many applications, e.g., bioinformatics, web access traces, system utilisation logs, etc., the data is naturally in the form of sequences. People have taken great interest in analysing the sequential data and finding the inherent characteristics or relationships within the data. Sequential association rule mining is one of the possible methods used to analyse this data. As conventional sequential association rule mining very often generates a huge number of association rules, of which many are redundant, it is desirable to find a solution to get rid of those unnecessary association rules. Because of the complexity and temporal ordered characteristics of sequential data, current research on sequential association rule mining is limited. Although several sequential association rule prediction models using either sequence constraints or temporal constraints have been proposed, none of them considered the redundancy problem in rule mining. The main contribution of this research is to propose a non-redundant association rule mining method based on closed frequent sequences and minimal sequential generators. We also give a definition for the non-redundant sequential rules, which are sequential rules with minimal antecedents but maximal consequents. A new algorithm called CSGM (closed sequential and generator mining) for generating closed sequences and minimal sequential generators is also introduced. A further experiment has been done to compare the performance of generating non-redundant sequential rules and full sequential rules, meanwhile, performance evaluation of our CSGM and other closed sequential pattern mining or generator mining algorithms has also been conducted. We also use generated non-redundant sequential rules for query expansion in order to improve recommendations for infrequently purchased products.
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Rahman, Anisur. "Rare sequential pattern mining of critical infrastructure control logs for anomaly detection." Thesis, Queensland University of Technology, 2019. https://eprints.qut.edu.au/132077/1/__qut.edu.au_Documents_StaffHome_staffgroupW%24_wu75_Documents_ePrints_Anisur_Rahman_Thesis_Redacted.pdf.

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Supervisory Control and Data Acquisition (SCADA) systems are used to drive much of a nation's critical infrastructure, which by definition is essential for the nation's citizens' way of life. They are connected to the computer networks and internet systems to operate, control and monitor their operations. This connectivity enables these SCADA systems to be exposed to cyber-attacks. This thesis detects anomalies or cyber-attacks on SCADA systems. It analyses SCADA control logs to find abnormal process activities which are treated as anomalies. A novel rare sequential pattern mining approach is proposed and developed to find rare or abnormal behaviour in SCADA systems.
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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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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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Wu, Sheng-Tang. "Knowledge discovery using pattern taxonomy model in text mining." Thesis, Queensland University of Technology, 2007. https://eprints.qut.edu.au/16675/1/Sheng-Tang_Wu_Thesis.pdf.

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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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Algarni, Abdulmohsen. "Relevance feature discovery for text analysis." Thesis, Queensland University of Technology, 2011. https://eprints.qut.edu.au/48230/1/Abdulmohsen_Algarni_Thesis.pdf.

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It is a big challenge to guarantee the quality of discovered relevance features in text documents for describing user preferences because of the large number of terms, patterns, and noise. Most existing popular text mining and classification methods have adopted term-based approaches. However, they have all suffered from the problems of polysemy and synonymy. Over the years, people have often held the hypothesis that pattern-based methods should perform better than term- based ones in describing user preferences, but many experiments do not support this hypothesis. This research presents a promising method, Relevance Feature Discovery (RFD), for solving this challenging issue. It discovers both positive and negative patterns in text documents as high-level features in order to accurately weight low-level features (terms) based on their specificity and their distributions in the high-level features. The thesis also introduces an adaptive model (called ARFD) to enhance the exibility of using RFD in adaptive environment. ARFD automatically updates the system's knowledge based on a sliding window over new incoming feedback documents. It can efficiently decide which incoming documents can bring in new knowledge into the system. Substantial experiments using the proposed models on Reuters Corpus Volume 1 and TREC topics show that the proposed models significantly outperform both the state-of-the-art term-based methods underpinned by Okapi BM25, Rocchio or Support Vector Machine and other pattern-based methods.
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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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Submitted by Aelson Maciera (aelsoncm@terra.com.br) on 2017-08-07T19:16:02Z No. of bitstreams: 1 DissRSJ.pdf: 7098556 bytes, checksum: 78b5b020899e1b4ef3e1fefb18d32443 (MD5)<br>Approved for entry into archive by Ronildo Prado (ronisp@ufscar.br) on 2017-08-07T19:18:39Z (GMT) No. of bitstreams: 1 DissRSJ.pdf: 7098556 bytes, checksum: 78b5b020899e1b4ef3e1fefb18d32443 (MD5)<br>Approved for entry into archive by Ronildo Prado (ronisp@ufscar.br) on 2017-08-07T19:18:50Z (GMT) No. of bitstreams: 1 DissRSJ.pdf: 7098556 bytes, checksum: 78b5b020899e1b4ef3e1fefb18d32443 (MD5)<br>Made available in DSpace on 2017-08-07T19:28:30Z (GMT). No. of bitstreams: 1 DissRSJ.pdf: 7098556 bytes, checksum: 78b5b020899e1b4ef3e1fefb18d32443 (MD5) Previous issue date: 2015-05-07<br>Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)<br>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.<br>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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Carvalho, Danilo Codeco. "Obtenção de padrões sequenciais em data streams atendendo requisitos do Big Data." Universidade Federal de São Carlos, 2016. https://repositorio.ufscar.br/handle/ufscar/8280.

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Submitted by Daniele Amaral (daniee_ni@hotmail.com) on 2016-10-20T18:13:56Z No. of bitstreams: 1 DissDCC.pdf: 2421455 bytes, checksum: 5fd16625959b31340d5f845754f109ce (MD5)<br>Approved for entry into archive by Marina Freitas (marinapf@ufscar.br) on 2016-11-08T18:42:36Z (GMT) No. of bitstreams: 1 DissDCC.pdf: 2421455 bytes, checksum: 5fd16625959b31340d5f845754f109ce (MD5)<br>Approved for entry into archive by Marina Freitas (marinapf@ufscar.br) on 2016-11-08T18:42:42Z (GMT) No. of bitstreams: 1 DissDCC.pdf: 2421455 bytes, checksum: 5fd16625959b31340d5f845754f109ce (MD5)<br>Made available in DSpace on 2016-11-08T18:42:49Z (GMT). No. of bitstreams: 1 DissDCC.pdf: 2421455 bytes, checksum: 5fd16625959b31340d5f845754f109ce (MD5) Previous issue date: 2016-06-06<br>Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)<br>The growing amount of data produced daily, by both businesses and individuals in the web, increased the demand for analysis and extraction of knowledge of this data. While the last two decades the solution was to store and perform data mining algorithms, currently it has become unviable even to supercomputers. In addition, the requirements of the Big Data age go far beyond the large amount of data to analyze. Response time requirements and complexity of the data acquire more weight in many areas in the real world. New models have been researched and developed, often proposing distributed computing or different ways to handle the data stream mining. Current researches shows that an alternative in the data stream mining is to join a real-time event handling mechanism with a classic mining association rules or sequential patterns algorithms. In this work is shown a data stream mining approach to meet the Big Data response time requirement, linking the event handling mechanism in real time Esper and Incremental Miner of Stretchy Time Sequences (IncMSTS) algorithm. The results show that is possible to take a static data mining algorithm for data stream environment and keep tendency in the patterns, although not possible to continuously read all data coming into the data stream.<br>O crescimento da quantidade de dados produzidos diariamente, tanto por empresas como por indivíduos na web, aumentou a exigência para a análise e extração de conhecimento sobre esses dados. Enquanto nas duas últimas décadas a solução era armazenar e executar algoritmos de mineração de dados, atualmente isso se tornou inviável mesmo em super computadores. Além disso, os requisitos da chamada era do Big Data vão muito além da grande quantidade de dados a se analisar. Requisitos de tempo de resposta e complexidade dos dados adquirem maior peso em muitos domínios no mundo real. Novos modelos têm sido pesquisados e desenvolvidos, muitas vezes propondo computação distribuída ou diferentes formas de se tratar a mineração de fluxo de dados. Pesquisas atuais mostram que uma alternativa na mineração de fluxo de dados é unir um mecanismo de tratamento de eventos em tempo real com algoritmos clássicos de mineração de regras de associação ou padrões sequenciais. Neste trabalho é mostrada uma abordagem de mineração de fluxo de dados (data stream) para atender ao requisito de tempo de resposta do Big Data, que une o mecanismo de manipulação de eventos em tempo real Esper e o algoritmo Incremental Miner of Stretchy Time Sequences (IncMSTS). Os resultados mostram ser possível levar um algoritmo de mineração de dados estático para o ambiente de fluxo de dados e manter as tendências de padrões encontrados, mesmo não sendo possível ler todos os dados vindos continuamente no fluxo de dados.
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Cano, Marcos Daniel. "Mineração de regras de associação sequenciais em séries temporais e visualização: aplicação em dados agrometeorológicos." Universidade Federal de São Carlos, 2012. https://repositorio.ufscar.br/handle/ufscar/564.

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Made available in DSpace on 2016-06-02T19:06:12Z (GMT). No. of bitstreams: 1 5971.pdf: 5628502 bytes, checksum: 38bfe45912e4f91f4ad8c7fb5fb815db (MD5) Previous issue date: 2012-08-03<br>Universidade Federal de Minas Gerais<br>Technological development brought improvements in the technology of climate sensors and Earth's surface image acquisition, gathering increasing amounts of data. Generally, when these data are submitted to mining algorithms, the output is the production of hundreds or even thousands of textual patterns, making the task of data analysis by the domain expert even harder. Hence, it is crucial, to support experts, the development of a tool that helps to identify and display patterns of interest. In this context, this research project at Master Science level aims to develop a technique for mining association rules in time series allowing agrometeorological data analysis over time.<br>O avanço tecnológico tem propiciado melhorias nos diversos sensores utilizados para medições dos dados climáticos e de imageamento da superfície terrestre, coletando quantidades cada vez maiores de dados. Quando esses dados são submetidos aos algoritmos de mineração para serem explorados ocorre, em geral, a produção de centenas ou ate mesmo milhares de padrões textuais, dificultando ainda mais a tarefa de analise dos dados pelo especialista de domínio. Assim, e crucial, para apoiar os especialistas, o desenvolvimento de um ferramental que auxilia na identificação e visualização dos padrões de interesse. Neste contexto, este projeto de pesquisa em nível de mestrado visa desenvolver uma técnica de mineração de regras de associação em series temporais permitindo a analise de dados agrometeorológicos ao longo do tempo.
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Cai, Chun Hing. "Mining association rules with weighted items." Hong Kong : Chinese University of Hong Kong, 1998. http://www.cse.cuhk.edu.hk/%7Ekdd/assoc%5Frule/thesis%5Fchcai.pdf.

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Thesis (M. Phil.)--Chinese University of Hong Kong, 1998.<br>Description based on contents viewed Mar. 13, 2007; title from title screen. Includes bibliographical references (p. 99-103). Also available in print.
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Books on the topic "Sequential Association Rules Mining"

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Adamo, Jean-Marc. Data Mining for Association Rules and Sequential Patterns. 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. Springer New York, 2001.

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

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Kaninis, A. Concurrent Mining of Association Rules. UMIST, 1997.

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Dass, Rajanish. Classification using association rules. Indian Institute of Management, 2008.

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1977-, Zhao Yanchang, Zhang Chengqi 1957-, and Cao Longbing 1969-, eds. Post-mining of association rules: Techniques for effective knowledge extraction. Information Science Reference, 2009.

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Data Mining for Association Rules and Sequential Patterns: Sequential and Parallel Algorithms. Springer, 2000.

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Observational Calculi and Association Rules Studies in Computational Intelligence. Springer, 2013.

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Inhibitory Rules In Data Analysis A Rough Set Approach. Springer, 2008.

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Book chapters on the topic "Sequential Association Rules Mining"

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

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

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

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

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

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

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

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

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Conference papers on the topic "Sequential Association Rules Mining"

1

Williams, Chad A., Abolfazl (Kouros) Mohammadian, Peter C. Nelson, and Sean T. Doherty. "Mining Sequential Association Rules for Traveler Context Prediction." In 5th International ICST Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services. ICST, 2008. http://dx.doi.org/10.4108/icst.mobiquitous2008.3867.

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Jiang, He, Runian Geng, and Baoyou Sun. "Research on Mining Sequential Positive and Negative Association Rules." In 2009 Second International Conference on Intelligent Computation Technology and Automation. IEEE, 2009. http://dx.doi.org/10.1109/icicta.2009.635.

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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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Chen, Yi-Chun, and Guanling Lee. "An efficient projected database method for mining sequential association rules." In 2010 Fifth International Conference on Digital Information Management (ICDIM). IEEE, 2010. http://dx.doi.org/10.1109/icdim.2010.5664724.

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Guo, Feng, Bencheng Cui, Lin Lin, and Cui Zhiquan. "Research on Mining Sequential Association Rules Based on Conditional Confidence." In 2021 IEEE International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC). IEEE, 2021. http://dx.doi.org/10.1109/sdpc52933.2021.9563390.

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Maruseac, Mihai, and Gabriel Ghinita. "Privacy-Preserving Mining of Sequential Association Rules from Provenance Workflows." In CODASPY'16: Sixth ACM Conference on Data and Application Security and Privacy. ACM, 2016. http://dx.doi.org/10.1145/2857705.2857743.

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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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Huang, Jih-Jeng. "An integrated method for mining association and sequential rules in distributed databases." In 2017 Joint 17th World Congress of International Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systems (IFSA-SCIS). IEEE, 2017. http://dx.doi.org/10.1109/ifsa-scis.2017.8023253.

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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}. 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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Zhou, Huiyu, Shingo Mabu, Kaoru Shimada, and Kotaro Hirasawa. "Generalized Time Related Sequential Association rule mining and traffic prediction." In 2009 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2009. http://dx.doi.org/10.1109/cec.2009.4983275.

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