Data Mining for Association Rules and Sequential Patterns

Data Mining for Association Rules and Sequential Patterns
Author: Jean-Marc Adamo
Publisher: Springer Science & Business Media
Total Pages: 259
Release: 2012-12-06
Genre: Computers
ISBN: 1461300851

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.

Frequent Pattern Mining

Frequent Pattern Mining
Author: Charu C. Aggarwal
Publisher: Springer
Total Pages: 480
Release: 2014-08-29
Genre: Computers
ISBN: 3319078216

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.

Advances in Artificial Intelligence

Advances in Artificial Intelligence
Author: Leila Kosseim
Publisher: Springer
Total Pages: 398
Release: 2012-05-09
Genre: Computers
ISBN: 9783642303524

This book constitutes the refereed proceedings of the 25th Canadian Conference on Artificial Intelligence, Canadian AI 2012, held in Toronto, Canada, in May 2012. The 23 regular papers, 16 short papers, and 4 papers from the Graduate Student Symposium presented were carefully reviewed and selected for inclusion in this book. The papers cover a broad range of topics presenting original work in all areas of artificial intelligence, either theoretical or applied.

Principles of Data Mining and Knowledge Discovery

Principles of Data Mining and Knowledge Discovery
Author: Jan Zytkow
Publisher: Springer Science & Business Media
Total Pages: 608
Release: 1999-09-01
Genre: Computers
ISBN: 3540664904

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.

High-Utility Pattern Mining

High-Utility Pattern Mining
Author: Philippe Fournier-Viger
Publisher: Springer
Total Pages: 343
Release: 2019-01-18
Genre: Technology & Engineering
ISBN: 3030049213

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.

Web Data Mining

Web Data Mining
Author: Bing Liu
Publisher: Springer Science & Business Media
Total Pages: 637
Release: 2011-06-25
Genre: Computers
ISBN: 3642194605

Liu has written a comprehensive text on Web mining, which consists of two parts. The first part covers the data mining and machine learning foundations, where all the essential concepts and algorithms of data mining and machine learning are presented. The second part covers the key topics of Web mining, where Web crawling, search, social network analysis, structured data extraction, information integration, opinion mining and sentiment analysis, Web usage mining, query log mining, computational advertising, and recommender systems are all treated both in breadth and in depth. His book thus brings all the related concepts and algorithms together to form an authoritative and coherent text. The book offers a rich blend of theory and practice. It is suitable for students, researchers and practitioners interested in Web mining and data mining both as a learning text and as a reference book. Professors can readily use it for classes on data mining, Web mining, and text mining. Additional teaching materials such as lecture slides, datasets, and implemented algorithms are available online.

Artificial Intelligence XXXVIII

Artificial Intelligence XXXVIII
Author: Max Bramer
Publisher: Springer Nature
Total Pages: 387
Release: 2021-12-06
Genre: Computers
ISBN: 3030911004

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.

R and Data Mining

R and Data Mining
Author: Yanchang Zhao
Publisher: Academic Press
Total Pages: 251
Release: 2012-12-31
Genre: Mathematics
ISBN: 012397271X

R and Data Mining introduces researchers, post-graduate students, and analysts to data mining using R, a free software environment for statistical computing and graphics. The book provides practical methods for using R in applications from academia to industry to extract knowledge from vast amounts of data. Readers will find this book a valuable guide to the use of R in tasks such as classification and prediction, clustering, outlier detection, association rules, sequence analysis, text mining, social network analysis, sentiment analysis, and more.Data mining techniques are growing in popularity in a broad range of areas, from banking to insurance, retail, telecom, medicine, research, and government. This book focuses on the modeling phase of the data mining process, also addressing data exploration and model evaluation.With three in-depth case studies, a quick reference guide, bibliography, and links to a wealth of online resources, R and Data Mining is a valuable, practical guide to a powerful method of analysis. - Presents an introduction into using R for data mining applications, covering most popular data mining techniques - Provides code examples and data so that readers can easily learn the techniques - Features case studies in real-world applications to help readers apply the techniques in their work

Machine Learning and Data Mining in Pattern Recognition

Machine Learning and Data Mining in Pattern Recognition
Author: Petra Perner
Publisher: Springer Science & Business Media
Total Pages: 837
Release: 2009-07-21
Genre: Computers
ISBN: 364203070X

There is no royal road to science, and only those who do not dread the fatiguing climb of its steep paths have a chance of gaining its luminous summits. Karl Marx A Universial Genius of the 19th Century Many scientists from all over the world during the past two years since the MLDM 2007 have come along on the stony way to the sunny summit of science and have worked hard on new ideas and applications in the area of data mining in pattern r- ognition. Our thanks go to all those who took part in this year's MLDM. We appre- ate their submissions and the ideas shared with the Program Committee. We received over 205 submissions from all over the world to the International Conference on - chine Learning and Data Mining, MLDM 2009. The Program Committee carefully selected the best papers for this year’s program and gave detailed comments on each submitted paper. There were 63 papers selected for oral presentation and 17 papers for poster presentation. The topics range from theoretical topics for classification, clustering, association rule and pattern mining to specific data-mining methods for the different multimedia data types such as image mining, text mining, video mining and Web mining. Among these topics this year were special contributions to subtopics such as attribute discre- zation and data preparation, novelty and outlier detection, and distances and simila- ties.