Frequent Pattern Mining

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Frequent Pattern Mining

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Author : Charu C. Aggarwal
language : en
Publisher: Springer
Release Date : 2014-08-29

Frequent Pattern Mining written by Charu C. Aggarwal and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-08-29 with Computers categories.

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.

Frequent Pattern Mining In Transactional And Structured Databases

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Author : Renáta Iváncsy
language : en
Publisher: LAP Lambert Academic Publishing
Release Date : 2010-10-01

Frequent Pattern Mining In Transactional And Structured Databases written by Renáta Iváncsy and has been published by LAP Lambert Academic Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-10-01 with categories.

Data mining is a process of discovering hidden relationships in large amounts of data. Frequent pattern discovery is an important research area in the field of data mining. Its purpose is to find patterns which appear frequently in a large collection of data. This work deals with three main areas of frequent pattern mining, namely, frequent itemset, frequent sequence and frequent subtree discovery. Beside providing a brief overview of related works of each single frequent pattern mining problem mentioned before, the three theses offered in this work suggest novel methods for efficient discovery of the different types of frequent patterns. The new methods are compared to the best-known algorithms in the related fields. The performance analysis of the methods involves measurements of the execution time and memory requirements.

Efficient Frequent Pattern Mining From Big Data And Its Applications

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Author : Fan Jiang
language : en
Publisher:
Release Date : 2014

Efficient Frequent Pattern Mining From Big Data And Its Applications written by Fan Jiang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with categories.

Frequent pattern mining is an important research areas in data mining. Since its introduction, it has drawn attention of many researchers. Consequently, many algorithms have been proposed. Popular algorithms include level-wise Apriori based algorithms, tree based algorithms, and hyperlinked array structure based algorithms. While these algorithms are popular and beneficial due to some nice properties, they also suffer from some drawbacks such as multiple database scans, recursive tree constructions, or multiple hyperlink adjustments. In the current era of big data, high volumes of a wide variety of valuable data of different veracities can be easily collected or generated at high velocity in various real-life applications. Among these 5V's of big data, I focus on handling high volumes of big data in my Ph.D. thesis. Specifically, I design and implement a new efficient frequent pattern mining algorithmic technique called B-mine, which overcomes some of the aforementioned drawbacks and achieves better performance when compared with existing algorithms. I also extend my B-mine algorithm into a family of algorithms that can perform big data mining efficiently. Moreover, I design four different frameworks that apply this family of algorithms to the real-life application of social network mining. Evaluation results show the efficiency and practicality of all these algorithms.

New Approaches To Weighted Frequent Pattern Mining

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Author : Unil Yun
language : en
Publisher:
Release Date : 2007

New Approaches To Weighted Frequent Pattern Mining written by Unil Yun and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with categories.

Researchers have proposed frequent pattern mining algorithms that are more efficient than previous algorithms and generate fewer but more important patterns. Many techniques such as depth first/breadth first search, use of tree/other data structures, top down/bottom up traversal and vertical/horizontal formats for frequent pattern mining have been developed. Most frequent pattern mining algorithms use a support measure to prune the combinatorial search space. However, support-based pruning is not enough when taking into consideration the characteristics of real datasets. Additionally, after mining datasets to obtain the frequent patterns, there is no way to adjust the number of frequent patterns through user feedback, except for changing the minimum support. Alternative measures for mining frequent patterns have been suggested to address these issues. One of the main limitations of the traditional approach for mining frequent patterns is that all items are treated uniformly when, in reality, items have different importance. For this reason, weighted frequent pattern mining algorithms have been suggested that give different weights to items according to their significance. The main focus in weighted frequent pattern mining concerns satisfying the downward closure property. In this research, frequent pattern mining approaches with weight constraints are suggested. Our main approach is to push weight constraints into the pattern growth algorithm while maintaining the downward closure property. We develop WFIM (Weighted Frequent Itemset Mining with a weight range and a minimum weight), WLPMiner (Weighted frequent Pattern Mining with length decreasing constraints), WIP (Weighted Interesting Pattern mining with a strong weight and/or support affinity), WSpan (Weighted Sequential pattern mining with a weight range and a minimum weight) and WIS (Weighted Interesting Sequential pattern mining with a similar level of support and/or weight affinity) The extensive performance analysis shows that suggested approaches are efficient and scalable in weighted frequent pattern mining.

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.

Frequent Pattern Mining With Wildcards

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Author : Yu He
language : en
Publisher:
Release Date : 2006

Frequent Pattern Mining With Wildcards written by Yu He and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006 with categories.

Design And Implementation Of Frequent Pattern Mining Toolkit Using Data Mining Template Library

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Author : Nilanjana De
language : en
Publisher:
Release Date : 2003

Design And Implementation Of Frequent Pattern Mining Toolkit Using Data Mining Template Library written by Nilanjana De and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003 with categories.

Fast Frequent Pattern Mining

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Author : Yabo Xu
language : en
Publisher:
Release Date : 2003

Fast Frequent Pattern Mining written by Yabo Xu and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003 with Data mining categories.

Sql Based Frequent Pattern Mining

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Author : Xuequn Shang
language : en
Publisher:
Release Date : 2005

Sql Based Frequent Pattern Mining written by Xuequn Shang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with categories.

Optimizations And Applications Of Trie Tree Based Frequent Pattern Mining

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Author : Stuart King
language : en
Publisher:
Release Date : 2006

Optimizations And Applications Of Trie Tree Based Frequent Pattern Mining written by Stuart King and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006 with Computer algorithms categories.