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.
Download or read book Proceedings of the Third SIAM International Conference on Data Mining written by Daniel Barbara and published by SIAM. This book was released on 2003-01-01 with total page 368 pages. Available in PDF, EPUB and Kindle. Book excerpt: The third SIAM International Conference on Data Mining provided an open forum for the presentation, discussion and development of innovative algorithms, software and theories for data mining applications and data intensive computation. This volume includes 21 research papers.
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 2005-02-28 with total page 188 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.
Download or read book Sequence Data Mining written by Guozhu Dong and published by Springer Science & Business Media. This book was released on 2007-10-31 with total page 160 pages. Available in PDF, EPUB and Kindle. Book excerpt: Understanding sequence data, and the ability to utilize this hidden knowledge, will create a significant impact on many aspects of our society. Examples of sequence data include DNA, protein, customer purchase history, web surfing history, and more. This book provides thorough coverage of the existing results on sequence data mining as well as pattern types and associated pattern mining methods. It offers balanced coverage on data mining and sequence data analysis, allowing readers to access the state-of-the-art results in one place.
Download or read book Advances in Database Technology EDBT 96 written by Mokrane Bouzeghoub and published by Springer Science & Business Media. This book was released on 1996-03-18 with total page 660 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the refereed proceedings of the Fifth International Conference on Extending Database Technology, EDBT'96, held in Avignon, France in March 1996. The 31 full revised papers included were selected from a total of 178 submissions; also included are some industrial-track papers, contributed by partners of several ESPRIT projects. The volume is organized in topical sections on data mining, active databases, design tools, advanced DBMS, optimization, warehousing, system issues, temporal databases, the web and hypermedia, performance, workflow management, database design, and parallel databases.
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.
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.
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.
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 2012-12-06 with total page 259 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.
Download or read book Pattern Discovery Using Sequence Data Mining written by Pradeep Kumar and published by . This book was released on 2011-07-01 with total page 272 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"--
Download or read book Mining of Massive Datasets written by Jure Leskovec and published by Cambridge University Press. This book was released on 2014-11-13 with total page 480 pages. Available in PDF, EPUB and Kindle. Book excerpt: Now in its second edition, this book focuses on practical algorithms for mining data from even the largest datasets.
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.
Download or read book Applications of Security Mobile Analytic and Cloud SMAC Technologies for Effective Information Processing and Management written by Karthikeyan, P. and published by IGI Global. This book was released on 2018-06-29 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: From cloud computing to big data to mobile technologies, there is a vast supply of information being mined and collected. With an abundant amount of information being accessed, stored, and saved, basic controls are needed to protect and prevent security incidents as well as ensure business continuity. Applications of Security, Mobile, Analytic, and Cloud (SMAC) Technologies for Effective Information Processing and Management is a vital resource that discusses various research findings and innovations in the areas of big data analytics, mobile communication and mobile applications, distributed systems, and information security. With a focus on big data, the internet of things (IoT), mobile technologies, cloud computing, and information security, this book proves a vital resource for computer engineers, IT specialists, software developers, researchers, and graduate-level students seeking current research on SMAC technologies and information security management systems.
Download or read book Advanced Data Mining and Applications written by Ronghuai Huang and published by Springer Science & Business Media. This book was released on 2009-07-28 with total page 826 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 5th International Conference on Advanced Data Mining and Applications, ADMA 2009, held in Beijing, China, in August 2009. The 34 revised full papers and 47 revised short papers presented together with the abstract of 4 keynote lectures were carefully reviewed and selected from 322 submissions from 27 countries. The papers focus on advancements in data mining and peculiarities and challenges of real world applications using data mining and feature original research results in data mining, spanning applications, algorithms, software and systems, and different applied disciplines with potential in data mining.
Download or read book Advances in Spatial and Temporal Databases written by Christian S. Jensen and published by Springer Science & Business Media. This book was released on 2001-07-02 with total page 532 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 7th International Conference on Spatial and Temporal Databases, SSTD 2001, held in Redondo Beach, CA, USA, in July 2001. The 25 revised full papers and two industrial papers presented were carefully reviewed and selected from a total of 70 submissions. The book offers topical sections on modeling and querying, moving-object query processing, query processing: architectures and cost estimation, processing advanced queries, formal aspects, data representation, industrial session, data warehousing and mining, and indexing.
Download or read book Advances in Knowledge Discovery and Data Mining written by Jian Pei and published by Springer. This book was released on 2013-03-20 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The two-volume set LNAI 7818 + LNAI 7819 constitutes the refereed proceedings of the 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2013, held in Gold Coast, Australia, in April 2013. The total of 98 papers presented in these proceedings was carefully reviewed and selected from 363 submissions. They cover the general fields of data mining and KDD extensively, including pattern mining, classification, graph mining, applications, machine learning, feature selection and dimensionality reduction, multiple information sources mining, social networks, clustering, text mining, text classification, imbalanced data, privacy-preserving data mining, recommendation, multimedia data mining, stream data mining, data preprocessing and representation.
Download or read book Advances in Knowledge Discovery and Data Mining Part I written by Mohammed J. Zaki and published by Springer Science & Business Media. This book was released on 2010-06 with total page 521 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 14th Pacific-Asia Conference, PAKDD 2010, held in Hyderabad, India, in June 2010.
