High Utility Pattern Mining
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High Utility Pattern Mining
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Author : Philippe Fournier-Viger
language : en
Publisher: Springer
Release Date : 2019-01-18
High Utility Pattern Mining written by Philippe Fournier-Viger and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-01-18 with Computers categories.
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.
Mining Sequential Patterns From Large Data Sets
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Author : Wei Wang
language : en
Publisher: Springer Science & Business Media
Release Date : 2005-07-26
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 2005-07-26 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.
Advances In Artificial Intelligence And Soft Computing
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Author : Grigori Sidorov
language : en
Publisher: Springer
Release Date : 2015-11-19
Advances In Artificial Intelligence And Soft Computing written by Grigori Sidorov and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-11-19 with Computers categories.
The two volume set LNAI 9413 + LNAI 9414 constitutes the proceedings of the 14th Mexican International Conference on Artificial Intelligence, MICAI 2015, held in Cuernavaca, Morelos, Mexico, in October 2015. The total of 98 papers presented in these proceedings was carefully reviewed and selected from 297 submissions. They were organized in topical sections named: natural language processing; logic and multi-agent systems; bioinspired algorithms; neural networks; evolutionary algorithms; fuzzy logic; machine learning and data mining; natural language processing applications; educational applications; biomedical applications; image processing and computer vision; search and optimization; forecasting; and intelligent applications.
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.
Web And Big Data
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Author : Lei Chen
language : en
Publisher: Springer
Release Date : 2017-08-01
Web And Big Data written by Lei Chen and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-08-01 with Computers categories.
This two –volume set, LNCS 10366 and 10367, constitutes the thoroughly refereed proceedings of the First International Joint Conference, APWeb-WAIM 2017, held in Beijing, China in July 2017. The 44 full papers presented together with 32 short papers and 10 demonstrations papers were carefully reviewed and selected from 240 submissions. The papers are organized around the following topics: spatial data processing and data quality; graph data processing; data mining, privacy and semantic analysis; text and log data management; social networks; data mining and data streams; query processing; topic modeling; machine learning; recommendation systems; distributed data processing and applications; machine learning and optimization.
The Top Ten Algorithms In Data Mining
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Author : Xindong Wu
language : en
Publisher: CRC Press
Release Date : 2009-04-09
The Top Ten Algorithms In Data Mining written by Xindong Wu and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-04-09 with Business & Economics categories.
Identifying some of the most influential algorithms that are widely used in the data mining community, The Top Ten Algorithms in Data Mining provides a description of each algorithm, discusses its impact, and reviews current and future research. Thoroughly evaluated by independent reviewers, each chapter focuses on a particular algorithm and is written by either the original authors of the algorithm or world-class researchers who have extensively studied the respective algorithm. The book concentrates on the following important algorithms: C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART. Examples illustrate how each algorithm works and highlight its overall performance in a real-world application. The text covers key topics—including classification, clustering, statistical learning, association analysis, and link mining—in data mining research and development as well as in data mining, machine learning, and artificial intelligence courses. By naming the leading algorithms in this field, this book encourages the use of data mining techniques in a broader realm of real-world applications. It should inspire more data mining researchers to further explore the impact and novel research issues of these algorithms.
Hands On Pattern Mining
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Author : Uday Kiran Rage
language : en
Publisher: Springer Nature
Release Date : 2025-08-11
Hands On Pattern Mining written by Uday Kiran Rage and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-08-11 with Computers categories.
This book introduces pattern mining by presenting various pattern mining techniques and giving hands-on experience with each technique. Pattern mining is a popular data mining technique with many real-world applications, and involves discovering all user interest-based patterns that may exist in a database. Several models and numerous algorithms were described in the literature to find these patterns in binary databases, quantitative databases, uncertain databases, and streams. Since the lack of a Python toolkit containing these algorithms has limited the wide adaptability of pattern-mining techniques, the author developed Pattern Mining (PAMI) Python library, which currently contains 80+ algorithms to discover useful patterns in transactional databases, temporal databases, quantitative databases, and graphs. The book consists of three main parts: !-- [if !supportLists]--· !--[endif]--Introduction: The first chapter introduces big data, types of learning techniques, and the importance of pattern mining. The second chapter introduces the PAMI library, its organizational structure, installation, and usage. !-- [if !supportLists]--· !--[endif]--Pattern mining algorithms and examples: The following chapters present the state-of-the-art techniques for discovering user interest-based patterns in (1) transactional databases, (2) temporal databases, (3) quantitative databases, (4) uncertain databases, (5) sequential databases, and (6) graphs. !-- [if !supportLists]--· !--[endif]--Applications: The book concludes with several applications, where the predicted knowledge using TensorFlow and PyTorch was transformed into a database to discover future trends or patterns.
2021 Tenth International Conference On Intelligent Computing And Information Systems Icicis
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Author : IEEE Staff
language : en
Publisher:
Release Date : 2021-12-05
2021 Tenth International Conference On Intelligent Computing And Information Systems Icicis written by IEEE Staff and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-12-05 with categories.
ICICIS 2021 is a premium International Conference that covers areas related to theory, development, application, and evaluation of advancements in computing technology It gathers researchers in these fields from all around the world This conference acts as an international forum for researchers and practitioners interested in the advances of artificial and computational intelligence as well as intelligent information systems It is an opportunity to present and observe the latest research, results and ideas in those areas The conference activities include distinguished keynote speakers, special sessions, exhibition, and social activities Themes of the conference Machine Learning, Artificial Intelligence, Pattern Recognition, internet of things, and data Visualization High Performance Computing, Robotics and Embedded Systems Big Data Analytics Bioinformatics Information Systems & Computer Network Cyber security &Block chain
Principles Of Data Mining
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Author : David J. Hand
language : en
Publisher: MIT Press
Release Date : 2001-08-17
Principles Of Data Mining written by David J. Hand and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001-08-17 with Computers categories.
The first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics. The growing interest in data mining is motivated by a common problem across disciplines: how does one store, access, model, and ultimately describe and understand very large data sets? Historically, different aspects of data mining have been addressed independently by different disciplines. This is the first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics. The book consists of three sections. The first, foundations, provides a tutorial overview of the principles underlying data mining algorithms and their application. The presentation emphasizes intuition rather than rigor. The second section, data mining algorithms, shows how algorithms are constructed to solve specific problems in a principled manner. The algorithms covered include trees and rules for classification and regression, association rules, belief networks, classical statistical models, nonlinear models such as neural networks, and local "memory-based" models. The third section shows how all of the preceding analysis fits together when applied to real-world data mining problems. Topics include the role of metadata, how to handle missing data, and data preprocessing.