Mining Sequential Patterns From Large Data Sets

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Mining Sequential Patterns from Large Data Sets

Wei Wang,Jiong Yang
Mining Sequential Patterns from Large Data Sets Book PDF

Author : Wei Wang,Jiong Yang
Publisher : Springer Science & Business Media
Page : 163 pages
File Size : 40,8 Mb
Release : 2006-03-30
Category : Computers
ISBN : 9780387242477

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Mining Sequential Patterns from Large Data Sets by Wei Wang,Jiong Yang Pdf

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.

Proceedings of the Third SIAM International Conference on Data Mining

Daniel Barbara,Chandrika Kamath
Proceedings of the Third SIAM International Conference on Data Mining Book PDF

Author : Daniel Barbara,Chandrika Kamath
Publisher : SIAM
Page : 368 pages
File Size : 45,7 Mb
Release : 2003-01-01
Category : Mathematics
ISBN : 0898715458

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Proceedings of the Third SIAM International Conference on Data Mining by Daniel Barbara,Chandrika Kamath Pdf

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.

Mining Sequential Patterns from Large Data Sets

Wei Wang,Jiong Yang
Mining Sequential Patterns from Large Data Sets Book PDF

Author : Wei Wang,Jiong Yang
Publisher : Springer Science & Business Media
Page : 188 pages
File Size : 54,6 Mb
Release : 2005-02-28
Category : Computers
ISBN : 0387242465

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Mining Sequential Patterns from Large Data Sets by Wei Wang,Jiong Yang Pdf

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.

Periodic Pattern Mining

R. Uday Kiran,Philippe Fournier-Viger,Jose M. Luna,Jerry Chun-Wei Lin,Anirban Mondal
Periodic Pattern Mining Book PDF

Author : R. Uday Kiran,Philippe Fournier-Viger,Jose M. Luna,Jerry Chun-Wei Lin,Anirban Mondal
Publisher : Springer Nature
Page : 263 pages
File Size : 50,5 Mb
Release : 2021-10-29
Category : Computers
ISBN : 9789811639647

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Periodic Pattern Mining by R. Uday Kiran,Philippe Fournier-Viger,Jose M. Luna,Jerry Chun-Wei Lin,Anirban Mondal Pdf

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.

Frequent Pattern Mining

Charu C. Aggarwal,Jiawei Han
Frequent Pattern Mining Book PDF

Author : Charu C. Aggarwal,Jiawei Han
Publisher : Springer
Page : 480 pages
File Size : 51,6 Mb
Release : 2014-08-29
Category : Computers
ISBN : 9783319078212

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Frequent Pattern Mining by Charu C. Aggarwal,Jiawei Han Pdf

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.

Data Mining for Association Rules and Sequential Patterns

Jean-Marc Adamo
Data Mining for Association Rules and Sequential Patterns Book PDF

Author : Jean-Marc Adamo
Publisher : Springer Science & Business Media
Page : 259 pages
File Size : 50,9 Mb
Release : 2012-12-06
Category : Computers
ISBN : 9781461300854

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Data Mining for Association Rules and Sequential Patterns by Jean-Marc Adamo Pdf

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.

High-Utility Pattern Mining

Philippe Fournier-Viger,Jerry Chun-Wei Lin,Roger Nkambou,Bay Vo,Vincent S. Tseng
High Utility Pattern Mining Book PDF

Author : Philippe Fournier-Viger,Jerry Chun-Wei Lin,Roger Nkambou,Bay Vo,Vincent S. Tseng
Publisher : Springer
Page : 337 pages
File Size : 51,7 Mb
Release : 2019-01-18
Category : Technology & Engineering
ISBN : 9783030049218

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High-Utility Pattern Mining by Philippe Fournier-Viger,Jerry Chun-Wei Lin,Roger Nkambou,Bay Vo,Vincent S. Tseng Pdf

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.

Pattern Discovery Using Sequence Data Mining

Pradeep Kumar,P. Radha Krishna,S. Bapi Raju
Pattern Discovery Using Sequence Data Mining Book PDF

Author : Pradeep Kumar,P. Radha Krishna,S. Bapi Raju
Publisher : Unknown
Page : 272 pages
File Size : 47,9 Mb
Release : 2011-07-01
Category : Sequential pattern mining
ISBN : 1613500580

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Pattern Discovery Using Sequence Data Mining by Pradeep Kumar,P. Radha Krishna,S. Bapi Raju Pdf

"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"--

Advances in Database Technology EDBT '96

Mokrane Bouzeghoub,Georges Gardarin
Advances in Database Technology EDBT 96 Book PDF

Author : Mokrane Bouzeghoub,Georges Gardarin
Publisher : Springer Science & Business Media
Page : 660 pages
File Size : 44,9 Mb
Release : 1996-03-18
Category : Business & Economics
ISBN : 354061057X

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Advances in Database Technology EDBT '96 by Mokrane Bouzeghoub,Georges Gardarin Pdf

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.

Sequence Data Mining

Guozhu Dong,Jian Pei
Sequence Data Mining Book PDF

Author : Guozhu Dong,Jian Pei
Publisher : Springer Science & Business Media
Page : 160 pages
File Size : 52,5 Mb
Release : 2007-10-31
Category : Computers
ISBN : 9780387699370

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Sequence Data Mining by Guozhu Dong,Jian Pei Pdf

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.

Applications of Security, Mobile, Analytic, and Cloud (SMAC) Technologies for Effective Information Processing and Management

Karthikeyan, P.,Thangavel, M.
Applications of Security Mobile Analytic and Cloud SMAC Technologies for Effective Information Processing and Management Book PDF

Author : Karthikeyan, P.,Thangavel, M.
Publisher : IGI Global
Page : 300 pages
File Size : 40,9 Mb
Release : 2018-06-29
Category : Computers
ISBN : 9781522540458

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Applications of Security, Mobile, Analytic, and Cloud (SMAC) Technologies for Effective Information Processing and Management by Karthikeyan, P.,Thangavel, M. Pdf

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.

Mining of Massive Datasets

Jure Leskovec,Jurij Leskovec,Anand Rajaraman,Jeffrey David Ullman
Mining of Massive Datasets Book PDF

Author : Jure Leskovec,Jurij Leskovec,Anand Rajaraman,Jeffrey David Ullman
Publisher : Cambridge University Press
Page : 480 pages
File Size : 44,7 Mb
Release : 2014-11-13
Category : Computers
ISBN : 9781107077232

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Mining of Massive Datasets by Jure Leskovec,Jurij Leskovec,Anand Rajaraman,Jeffrey David Ullman Pdf

Now in its second edition, this book focuses on practical algorithms for mining data from even the largest datasets.

Advanced Data Mining and Applications

Ronghuai Huang,Qiang Yang,Jian Pei,João Gama,Xiaofeng Meng,Xue Li
Advanced Data Mining and Applications Book PDF

Author : Ronghuai Huang,Qiang Yang,Jian Pei,João Gama,Xiaofeng Meng,Xue Li
Publisher : Springer Science & Business Media
Page : 826 pages
File Size : 50,9 Mb
Release : 2009-07-28
Category : Computers
ISBN : 9783642033476

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Advanced Data Mining and Applications by Ronghuai Huang,Qiang Yang,Jian Pei,João Gama,Xiaofeng Meng,Xue Li Pdf

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.

Mining Big Data for Frequent Patterns Using MapReduce Computing

Sumalatha Saleti
Mining Big Data for Frequent Patterns Using MapReduce Computing Book PDF

Author : Sumalatha Saleti
Publisher : Unknown
Page : 0 pages
File Size : 44,6 Mb
Release : 2023-08-31
Category : Computers
ISBN : 8119549600

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Mining Big Data for Frequent Patterns Using MapReduce Computing by Sumalatha Saleti Pdf

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.

Principles of Data Mining and Knowledge Discovery

Jan Zytkow,Jan Rauch
Principles of Data Mining and Knowledge Discovery Book PDF

Author : Jan Zytkow,Jan Rauch
Publisher : Springer Science & Business Media
Page : 608 pages
File Size : 45,9 Mb
Release : 1999-09-01
Category : Computers
ISBN : 9783540664901

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Principles of Data Mining and Knowledge Discovery by Jan Zytkow,Jan Rauch Pdf

This book constitutes the refereed proceedings of the Third European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD'99, held in Prague, Czech Republic in September 1999. The 28 revised full papers and 48 poster presentations were carefully reviewed and selected from 106 full papers submitted. The papers are organized in topical sections on time series, applications, taxonomies and partitions, logic methods, distributed and multirelational databases, text mining and feature selection, rules and induction, and interesting and unusual issues.