Periodic Pattern Mining

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Periodic Pattern Mining

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Author : R. Uday Kiran
language : en
Publisher: Springer Nature
Release Date : 2021-10-29

Periodic Pattern Mining written by R. Uday Kiran and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10-29 with Computers categories.

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.

A Study On Efficient Algorithms For Partial Periodic Pattern Mining

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Author : 楊恭娟
language : en
Publisher:
Release Date : 2013

A Study On Efficient Algorithms For Partial Periodic Pattern Mining written by 楊恭娟 and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with categories.

Principles Of Data Mining And Knowledge Discovery

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Author :
language : en
Publisher:
Release Date : 1998

Principles Of Data Mining And Knowledge Discovery written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1998 with Data mining categories.

2002 Ieee International Conference On Data Mining

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Author : Vipin Kumar
language : en
Publisher: IEEE Computer Society Press
Release Date : 2002

2002 Ieee International Conference On Data Mining written by Vipin Kumar and has been published by IEEE Computer Society Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002 with Computers categories.

Consists of 72 full papers and 49 short papers from the December 2002 conference on the design, analysis, and implementation of data mining theory, systems, and applications. Topics of the full papers include evolutionary time series segmentation for stock data mining, cluster merging and splitting

Pfpm Discovering Periodic Frequent Patterns With Novel Periodicity Measures

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Author : Philippe Fournier-Viger
language : en
Publisher:
Release Date : 2017

Pfpm Discovering Periodic Frequent Patterns With Novel Periodicity Measures written by Philippe Fournier-Viger and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with Technology categories.

Periodic pattern mining is the task of discovering patterns that periodically appear in transactions. Typically, periodic pattern mining algorithms will discard a pattern as being nonperiodic if it has a single period greater than a maximal periodicity threshold, defined by the user. A major drawback of this approach is that it is not flexible, as a pattern can be discarded based on only one of its periods. In this chapter, we present a solution to this issue by proposing to discover periodic patterns using three measures: the minimum periodicity, the maximum periodicity, and the average periodicity. The combination of these measures has the advantage of being more flexible. Properties of these measures are studied. Moreover, an efficient algorithm named PFPM (Periodic Frequent Pattern Miner) is proposed to discover all frequent periodic patterns using these measures. An experimental evaluation on real data sets shows that the proposed PFPM algorithm is efficient and can filter a huge number of nonperiodic patterns to reveal only the desired periodic patterns.

Proceedings Of The Third Siam International Conference On Data Mining

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Author : Daniel Barbara
language : en
Publisher: Soc for Industrial & Applied Math
Release Date : 2003

Proceedings Of The Third Siam International Conference On Data Mining written by Daniel Barbara and has been published by Soc for Industrial & Applied Math this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003 with Computers categories.

We are very pleased to present the proceedings of the 2003 SIAM International Conference on Data Mining. The field of Data Mining has seen a tremendous increase of interest in recent months. Applications of Data Mining are mentioned often in the daily press, especially in the fields of security and forensics. Thus, these are exciting times for researchers and practitioners in the area. We hope that the research captured by these proceedings helps in advancing this important field.

Mining Partial Periodic Pattern With Random Replacement

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Author : International Business Machines Corporation. Research Division. (IBMRD)
language : en
Publisher:
Release Date : 2000

Mining Partial Periodic Pattern With Random Replacement written by International Business Machines Corporation. Research Division. (IBMRD) and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000 with Data mining categories.

Abstract: "In this paper, we focus on mining periodic patterns allowing some degree of imperfection in the form of random replacement from a perfect periodic pattern. Instead of using the traditional metrics such as support and confidence, a more meaningful metric, information gain, is introduced which can naturally identify patterns with events of vastly different occurrence frequencies and adjust for the deviation from a pattern. In fact, a pattern can be of arbitrary length and may repeat itself over a contiguous portion of the sequence. We developed an effective mining algorithm which decomposed the problem into first finding periodic patterns consisting of a single event with any arbitrary period and then obtaining composite patterns of an arbitrary period with multiple events. Advanced pruning techniques are developed to tackle the predicament caused by the violation of the downward closure property by the information gain measure and in turn provides an efficient solution to this problem."

Temporal And Spatio Temporal Data Mining

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Author : Wynne Hsu
language : en
Publisher: IGI Global
Release Date : 2008

Temporal And Spatio Temporal Data Mining written by Wynne Hsu and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with Computers categories.

"This book presents probable solutions when discovering the spatial sequence patterns by incorporating the information into the sequence of patterns, and introduces new classes of spatial sequence patterns, called flow and generalized spatio-temporal patterns, addressing different scenarios in spatio-temporal data by modeling them as graphs, providing a comprehensive synopsis on two successful partition-based algorithms designed by the authors"--Provided by publisher.

Data Warehousing And Knowledge Discovery

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Author :
language : en
Publisher:
Release Date : 2004

Data Warehousing And Knowledge Discovery written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004 with Data mining categories.

Mining Sequential Patterns From Large Data Sets

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Author : Wei Wang
language : en
Publisher: Springer Science & Business Media
Release Date : 2005-02-28

Mining Sequential Patterns From Large Data Sets written by Wei Wang and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-02-28 with Computers categories.

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.