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Sequential Pattern Generalization For Mining Multi Source Data
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Book Synopsis Sequential Pattern Generalization for Mining Multi-source Data by : Julie Bu Daher
Download or read book Sequential Pattern Generalization for Mining Multi-source Data written by Julie Bu Daher and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Huge amounts of digital data have been created across years due to the increasing digitization in our everyday life. As a consequence, fast data collection and storage tools have been developed and data can be collected in huge volumes for various research and business purposes. The collected data can come from multiple data sources and can be of heterogeneous kinds thus forming heterogeneous multi-source datasets, and they can be analyzed to extract valuable information. Data mining is an important task in discovering interesting information from datasets. Different approaches in this domain have been proposed, among which pattern mining is the most important one. Pattern mining, including sequential pattern mining, discovers statistically relevant patterns (or sequential patterns) among data. The challenges of this domain include discovering important patterns with a limited complexity and by avoiding redundancy among the resulting patterns. Multi-source data could represent descriptive and sequential data, making the mining process complex. There could be problems of data similarity on one source level which leads to a limited number of extracted patterns. The aim of the thesis is to mine multi-source data to obtain valuable information and compensate the loss of patterns due to the problem of similarity with a limited complexity and by avoiding pattern redundancy. Many approaches have been proposed to mine multi-source data. These approaches either integrate multi-source data and perform a single mining process which increases the complexity and generates a redundant set of sequential patterns, or they mine sources separately and integrate the results which could lead to a loss of patterns. We propose G_SPM, a general sequential pattern mining algorithm that takes advantage of multi-source data to mine general patterns which compensates the loss of patterns caused by the problem of data similarity. These rich patterns contain various kinds of information and have higher data coverage than traditional patterns. G_SPM adopts a selective mining strategy of data sources where a main source is first mined, and other sources are mined only when similarity among patterns is detected, which limits the complexity and avoids pattern redundancy. The experimental results confirm that G_SPM succeeds in mining general patterns with a limited complexity. In addition, it outperforms traditional approaches in terms of runtime and pattern redundancy.
Book Synopsis Mining Sequential Patterns from Large Data Sets by : Wei Wang
Download or read book Mining Sequential Patterns from Large Data Sets written by Wei Wang and published by Springer Science & Business Media. This book was released on 2005-07-26 with total page 174 pages. Available in PDF, EPUB and Kindle. Book excerpt: In many applications, e.g., bioinformatics, web access traces, system u- lization logs, etc., the data is naturally in the form of sequences. It has been of great interests to analyze the sequential data to find their inherent char- teristics. The sequential pattern is one of the most widely studied models to capture such characteristics. Examples of sequential patterns include but are not limited to protein sequence motifs and web page navigation traces. In this book, we focus on sequential pattern mining. To meet different needs of various applications, several models of sequential patterns have been proposed. We do not only study the mathematical definitions and application domains of these models, but also the algorithms on how to effectively and efficiently find these patterns. The objective of this book is to provide computer scientists and domain - perts such as life scientists with a set of tools in analyzing and understanding the nature of various sequences by : (1) identifying the specific model(s) of - quential patterns that are most suitable, and (2) providing an efficient algorithm for mining these patterns. Chapter 1 INTRODUCTION Data Mining is the process of extracting implicit knowledge and discovery of interesting characteristics and patterns that are not explicitly represented in the databases. The techniques can play an important role in understanding data and in capturing intrinsic relationships among data instances. Data mining has been an active research area in the past decade and has been proved to be very useful.
Book Synopsis Periodic Pattern Mining by : R. Uday Kiran
Download or read book Periodic Pattern Mining written by R. Uday Kiran and published by Springer Nature. This book was released on 2021-10-29 with total page 263 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an introduction to the field of periodic pattern mining, reviews state-of-the-art techniques, discusses recent advances, and reviews open-source software. Periodic pattern mining is a popular and emerging research area in the field of data mining. It involves discovering all regularly occurring patterns in temporal databases. One of the major applications of periodic pattern mining is the analysis of customer transaction databases to discover sets of items that have been regularly purchased by customers. Discovering such patterns has several implications for understanding the behavior of customers. Since the first work on periodic pattern mining, numerous studies have been published and great advances have been made in this field. The book consists of three main parts: introduction, algorithms, and applications. The first chapter is an introduction to pattern mining and periodic pattern mining. The concepts of periodicity, periodic support, search space exploration techniques, and pruning strategies are discussed. The main types of algorithms are also presented such as periodic-frequent pattern growth, partial periodic pattern-growth, and periodic high-utility itemset mining algorithm. Challenges and research opportunities are reviewed. The chapters that follow present state-of-the-art techniques for discovering periodic patterns in (1) transactional databases, (2) temporal databases, (3) quantitative temporal databases, and (4) big data. Then, the theory on concise representations of periodic patterns is presented, as well as hiding sensitive information using privacy-preserving data mining techniques. The book concludes with several applications of periodic pattern mining, including applications in air pollution data analytics, accident data analytics, and traffic congestion analytics.
Book Synopsis Mining Sequential Patterns: Generalizations and Performance Improvements by : International Business Machines Corporation. Research Division
Download or read book Mining Sequential Patterns: Generalizations and Performance Improvements written by International Business Machines Corporation. Research Division and published by . This book was released on 1996 with total page 29 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: "The problem of mining sequential patterns was recently introduced in [AS95]. We are given a database of sequences, where each sequence is a list of transactions ordered by transaction-time, and each transaction is a set of items. The problem is to discover all sequential patterns with a user-specified minimum support, where the support of a pattern is the number of data-sequences that contain the pattern. An example of a sequential pattern is '5% of customers bought 'Foundation' and 'Ringworld' in one transaction, followed by 'Second Foundation' in a later transaction'. We generalize the problem as follows. First, we add time constraints that specify a minimum and/or maximum time period between adjacent elements in a pattern. Second, we relax the restriction that the items in an element of a sequential pattern must come from the same transaction, instead allowing the items to be present in a set of transactions whose transaction-times are within a user-specified time window. Third, given a user-defined taxonomy (is-a hierarchy) on items, we allow sequential patterns to include items across all levels of the taxonomy. We present GSP, a new algorithm that discovers these generalized sequential patterns. Empirical evaluation using synthetic and real-life data indicates that GSP is much faster than the AprioriAll algorithm presented in [AS95]. GSP scales linearly with the number of data- sequences, and has very good scale-up properties with respect to the average data-sequence size."
Book Synopsis High-Utility Pattern Mining by : Philippe Fournier-Viger
Download or read book High-Utility Pattern Mining written by Philippe Fournier-Viger and published by Springer. This book was released on 2019-01-18 with total page 337 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents an overview of techniques for discovering high-utility patterns (patterns with a high importance) in data. It introduces the main types of high-utility patterns, as well as the theory and core algorithms for high-utility pattern mining, and describes recent advances, applications, open-source software, and research opportunities. It also discusses several types of discrete data, including customer transaction data and sequential data. The book consists of twelve chapters, seven of which are surveys presenting the main subfields of high-utility pattern mining, including itemset mining, sequential pattern mining, big data pattern mining, metaheuristic-based approaches, privacy-preserving pattern mining, and pattern visualization. The remaining five chapters describe key techniques and applications, such as discovering concise representations and regular patterns.
Book Synopsis Frequent Pattern Mining by : Charu C. Aggarwal
Download or read book Frequent Pattern Mining written by Charu C. Aggarwal and published by Springer. This book was released on 2014-08-29 with total page 480 pages. Available in PDF, EPUB and Kindle. Book excerpt: This comprehensive reference consists of 18 chapters from prominent researchers in the field. Each chapter is self-contained, and synthesizes one aspect of frequent pattern mining. An emphasis is placed on simplifying the content, so that students and practitioners can benefit from the book. Each chapter contains a survey describing key research on the topic, a case study and future directions. Key topics include: Pattern Growth Methods, Frequent Pattern Mining in Data Streams, Mining Graph Patterns, Big Data Frequent Pattern Mining, Algorithms for Data Clustering and more. Advanced-level students in computer science, researchers and practitioners from industry will find this book an invaluable reference.
Book Synopsis Data Mining for Association Rules and Sequential Patterns by : Jean-Marc Adamo
Download or read book Data Mining for Association Rules and Sequential Patterns written by Jean-Marc Adamo and published by Springer Science & Business Media. This book was released on 2001 with total page 268 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recent advances in data collection, storage technologies, and computing power have made it possible for companies, government agencies and scientific laboratories to keep and manipulate vast amounts of data relating to their activities. This state-of-the-art monograph discusses essential algorithms for sophisticated data mining methods used with large-scale databases, focusing on two key topics: association rules and sequential pattern discovery. This will be an essential book for practitioners and professionals in computer science and computer engineering.
Book Synopsis Developing Multi-Database Mining Applications by : Animesh Adhikari
Download or read book Developing Multi-Database Mining Applications written by Animesh Adhikari and published by Springer Science & Business Media. This book was released on 2010-06-14 with total page 134 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multi-database mining has been recognized recently as an important and strategically essential area of research in data mining. In this book, we discuss various issues regarding the systematic and efficient development of multi-database mining applications. It explains how systematically one could prepare data warehouses at different branches. As appropriate multi-database mining technique is essential to develop better applications. Also, the efficiency of a multi-database mining application could be improved by processing more patterns in the application. A faster algorithm could also play an important role in developing a better application. Thus the efficiency of a multi-database mining application could be enhanced by choosing an appropriate multi-database mining model, an appropriate pattern synthesizing technique, a better pattern representation technique, and an efficient algorithm for solving the problem. This book illustrates each of these issues either in the context of a specific problem, or in general.
Book Synopsis Data Mining Patterns: New Methods and Applications by : Poncelet, Pascal
Download or read book Data Mining Patterns: New Methods and Applications written by Poncelet, Pascal and published by IGI Global. This book was released on 2007-08-31 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book provides an overall view of recent solutions for mining, and explores new patterns,offering theoretical frameworks and presenting challenges and possible solutions concerning pattern extractions, emphasizing research techniques and real-world applications. It portrays research applications in data models, methodologies for mining patterns, multi-relational and multidimensional pattern mining, fuzzy data mining, data streaming and incremental mining"--Provided by publisher.
Book Synopsis Encyclopedia of Data Warehousing and Mining, Second Edition by : Wang, John
Download or read book Encyclopedia of Data Warehousing and Mining, Second Edition written by Wang, John and published by IGI Global. This book was released on 2008-08-31 with total page 2542 pages. Available in PDF, EPUB and Kindle. Book excerpt: There are more than one billion documents on the Web, with the count continually rising at a pace of over one million new documents per day. As information increases, the motivation and interest in data warehousing and mining research and practice remains high in organizational interest. The Encyclopedia of Data Warehousing and Mining, Second Edition, offers thorough exposure to the issues of importance in the rapidly changing field of data warehousing and mining. This essential reference source informs decision makers, problem solvers, and data mining specialists in business, academia, government, and other settings with over 300 entries on theories, methodologies, functionalities, and applications.
Book Synopsis Data Analysis and Pattern Recognition in Multiple Databases by : Animesh Adhikari
Download or read book Data Analysis and Pattern Recognition in Multiple Databases written by Animesh Adhikari and published by Springer Science & Business Media. This book was released on 2013-12-09 with total page 247 pages. Available in PDF, EPUB and Kindle. Book excerpt: Pattern recognition in data is a well known classical problem that falls under the ambit of data analysis. As we need to handle different data, the nature of patterns, their recognition and the types of data analyses are bound to change. Since the number of data collection channels increases in the recent time and becomes more diversified, many real-world data mining tasks can easily acquire multiple databases from various sources. In these cases, data mining becomes more challenging for several essential reasons. We may encounter sensitive data originating from different sources - those cannot be amalgamated. Even if we are allowed to place different data together, we are certainly not able to analyze them when local identities of patterns are required to be retained. Thus, pattern recognition in multiple databases gives rise to a suite of new, challenging problems different from those encountered before. Association rule mining, global pattern discovery and mining patterns of select items provide different patterns discovery techniques in multiple data sources. Some interesting item-based data analyses are also covered in this book. Interesting patterns, such as exceptional patterns, icebergs and periodic patterns have been recently reported. The book presents a thorough influence analysis between items in time-stamped databases. The recent research on mining multiple related databases is covered while some previous contributions to the area are highlighted and contrasted with the most recent developments.
Book Synopsis Advanced Data Mining and Applications by : Jie Tang
Download or read book Advanced Data Mining and Applications written by Jie Tang and published by Springer. This book was released on 2011-12-15 with total page 434 pages. Available in PDF, EPUB and Kindle. Book excerpt: The two-volume set LNAI 7120 and LNAI 7121 constitutes the refereed proceedings of the 7th International Conference on Advanced Data Mining and Applications, ADMA 2011, held in Beijing, China, in December 2011. The 35 revised full papers and 29 short papers presented together with 3 keynote speeches were carefully reviewed and selected from 191 submissions. The papers cover a wide range of topics presenting original research findings in data mining, spanning applications, algorithms, software and systems, and applied disciplines.
Book Synopsis Pattern Discovery Using Sequence Data Mining by : Pradeep Kumar
Download or read book Pattern Discovery Using Sequence Data Mining written by Pradeep Kumar and published by IGI Global. This book was released on 2012 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book provides a comprehensive view of sequence mining techniques, and present current research and case studies in Pattern Discovery in Sequential data authored by researchers and practitioners"--
Book Synopsis Advances in Knowledge Discovery and Data Mining by : Kamal Karlapalem
Download or read book Advances in Knowledge Discovery and Data Mining written by Kamal Karlapalem and published by Springer Nature. This book was released on 2021-05-08 with total page 865 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 3-volume set LNAI 12712-12714 constitutes the proceedings of the 25th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2021, which was held during May 11-14, 2021. The 157 papers included in the proceedings were carefully reviewed and selected from a total of 628 submissions. They were organized in topical sections as follows: Part I: Applications of knowledge discovery and data mining of specialized data; Part II: Classical data mining; data mining theory and principles; recommender systems; and text analytics; Part III: Representation learning and embedding, and learning from data.
Book Synopsis Computational Collective Intelligence by : Ngoc Thanh Nguyen
Download or read book Computational Collective Intelligence written by Ngoc Thanh Nguyen and published by Springer Nature. This book was released on 2020-11-23 with total page 908 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume constitutes the refereed proceedings of the 12th International Conference on Computational Collective Intelligence, ICCCI 2020, held in Da Nang, Vietnam, in November 2020.* The 70 full papers presented were carefully reviewed and selected from 314 submissions. The papers are grouped in topical sections on: knowledge engineering and semantic web; social networks and recommender systems; collective decision-making; applications of collective intelligence; data mining methods and applications; machine learning methods; deep learning and applications for industry 4.0; computer vision techniques; biosensors and biometric techniques; innovations in intelligent systems; natural language processing; low resource languages processing; computational collective intelligence and natural language processing; computational intelligence for multimedia understanding; and intelligent processing of multimedia in web systems. *The conference was held virtually due to the COVID-19 pandemic.
Book Synopsis Mining Big Data for Frequent Patterns Using MapReduce Computing by : Sumalatha Saleti
Download or read book Mining Big Data for Frequent Patterns Using MapReduce Computing written by Sumalatha Saleti and published by . This book was released on 2023-08-31 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The main motivation of frequent pattern mining is to extract useful patterns from the data sets. Interesting associations among the data can be discovered by mining the frequent patterns. Among the different kinds of pattern mining, frequent itemset mining has been applied widely in many applications such as market basket analysis, medical applications, online transactions, social network analysis and so forth. An itemset is called frequent if the set of items in it appear frequently together. However, frequent itemset mining can find only the frequent itemsets, the time regularity of the items cannot be found. Sequential pattern mining considers both the frequency of the items and the order of items based on their time stamps. It attracted great deal of attention in many applications such as customer buying trend analysis, web access mining, natural disaster analysis and so forth. The patterns mined from sequential pattern mining algorithms do not consider the cost or profit of the item. A sequence that is not frequent in a dataset may contribute much to the overall profit of the organization due to its high profit. Hence, utility sequential pattern mining considers quantity and timestamp of items as well as profit of each item. Because of constantly arriving new data, the resultant patterns of frequent pattern mining may become obsolete over time. Hence, it is necessary to incrementally process the data in order to refresh the mining results without mining from scratch. The advancement in technology led to the generation of huge volumes of data from multiple sources such as social media, online transactions, internet applications and so forth. This era of big data pose a challenge to explore large volumes of data and extract the knowledge in the form of useful patterns. Moreover, the conventional methods used in mining patterns are not suitable for handling the big data. Hence, in this thesis, we investigate the solutions for frequent pattern mining on big data using a popular programming model known as MapReduce. Firstly, we propose a parallel algorithm for compressing the transactional data that makes the data simple and Bit Vector Product algorithm is proposed to mine the frequent itemsets from the compressed data. Secondly, distributed algorithm for mining sequential patterns using cooccurrence information is proposed. Here, we make use of item co-occurrence information and reduce the search space using the pruning strategies. Thirdly, distributed high utility time interval sequential patterns with time information between the successive items are mined. Finally, an incremental algorithm is proposed to make use of the knowledge obtained in ii previous mining while mining sequential patterns. All the proposed algorithms are tested on our in house Hadoop cluster composed of one master node and eight data nodes.
Book Synopsis Advanced Data Mining and Applications by : Hiroshi Motoda
Download or read book Advanced Data Mining and Applications written by Hiroshi Motoda and published by Springer. This book was released on 2013-12-14 with total page 605 pages. Available in PDF, EPUB and Kindle. Book excerpt: The two-volume set LNAI 8346 and 8347 constitutes the thoroughly refereed proceedings of the 9th International Conference on Advanced Data Mining and Applications, ADMA 2013, held in Hangzhou, China, in December 2013. The 32 regular papers and 64 short papers presented in these two volumes were carefully reviewed and selected from 222 submissions. The papers included in these two volumes cover the following topics: opinion mining, behavior mining, data stream mining, sequential data mining, web mining, image mining, text mining, social network mining, classification, clustering, association rule mining, pattern mining, regression, predication, feature extraction, identification, privacy preservation, applications, and machine learning.
