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Author: Jean-Marc Adamo
Publisher: Springer Science & Business Media
ISBN: 1461300851
Category : Computers
Languages : en
Pages : 259
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Book Description
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.
Author: Jean-Marc Adamo
Publisher: Springer Science & Business Media
ISBN: 1461300851
Category : Computers
Languages : en
Pages : 259
Get Book
Book Description
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.
Author: Charu C. Aggarwal
Publisher: Springer
ISBN: 3319078216
Category : Computers
Languages : en
Pages : 471
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Book Description
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.
Author: Wei Wang
Publisher: Springer Science & Business Media
ISBN: 0387242473
Category : Computers
Languages : en
Pages : 163
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Book Description
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.
Author: Mokrane Bouzeghoub
Publisher: Springer Science & Business Media
ISBN: 9783540610571
Category : Business & Economics
Languages : en
Pages : 660
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Book Description
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.
Author: Chengqi Zhang
Publisher: Springer
ISBN: 3540460276
Category : Computers
Languages : en
Pages : 244
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Book Description
Due to the popularity of knowledge discovery and data mining, in practice as well as among academic and corporate R&D professionals, association rule mining is receiving increasing attention. The authors present the recent progress achieved in mining quantitative association rules, causal rules, exceptional rules, negative association rules, association rules in multi-databases, and association rules in small databases. This book is written for researchers, professionals, and students working in the fields of data mining, data analysis, machine learning, knowledge discovery in databases, and anyone who is interested in association rule mining.
Author: Mohammed J. Zaki
Publisher: Springer Science & Business Media
ISBN: 3642136567
Category : Computers
Languages : en
Pages : 521
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Book Description
This book constitutes the proceedings of the 14th Pacific-Asia Conference, PAKDD 2010, held in Hyderabad, India, in June 2010.
Author: Dr. Suneel Pappala
Publisher: Blue Rose Publishers
ISBN:
Category : Computers
Languages : en
Pages : 45
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Book Description
DATA MINING IS USE TO COMPUTER SCIENCE AND ENGINEERING AND INFORMATION TECHNOLOGY STUDENTS. Data Mining is The process of automatically discovering useful information in large data repositories. – Observation = case, record, instance – Variable = field, attribute – Analysis of dependence vs interdependence = Supervised vs unsupervised learning – Relationship = association, concept – Dependent variable Data Mining is mainly concentrated on Association rule, Mining Frequent Patterns it is concentrated on Associations and correlations and also concentrated on Mining Methods,Mining Various kinds of Association Rules,Correlation Analysis, Constraint based Association mining. Graph Pattern Mining SPM. Classification and Prediction ,Basic concepts,Decision tree induction,Bayesian classification, Rule–based classification, Lazy learner. Cluster analysis,Types of Data in Cluster Analysis,Categorization of Major Clustering Methods, Partitioning Methods, Hierarchical Methods,Density Based Methods, Grid Based Methods, Outlier Analysis. Basic concepts in Mining data streams Mining Time series data Mining sequence patterns in Transactional databases Mining Object Spatial Multimedia Text and Web data Spatial Data mining Multimedia Data mining Text Mining Mining the World Wide Web.
Author: Jan Zytkow
Publisher: Springer Science & Business Media
ISBN: 3540664904
Category : Computers
Languages : en
Pages : 608
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Book Description
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.
Author: R. Uday Kiran
Publisher: Springer Nature
ISBN: 9811639647
Category : Computers
Languages : en
Pages : 263
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Book Description
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.
Author: Max Bramer
Publisher: Springer Nature
ISBN: 3030911004
Category : Computers
Languages : en
Pages : 387
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Book Description
This book constitutes the proceedings of the 41st SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, AI 2021, which was supposed to be held in Cambridge, UK, in December 2021. The conference was held virtually due to the COVID-19 pandemic. The 22 full papers and 10 short papers presented in this volume were carefully reviewed and selected from 37 submissions. The volume includes technical papers presenting new and innovative developments in the field as well as application papers presenting innovative applications of AI techniques in a number of subject domains. The papers are organized in the following topical sections: technical paper; machine learning; AI techniques; short technical stream papers; application papers; applications of machine learning; AI for medicine; advances in applied AI; and short application stream papers.
