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Research and Development in Knowledge Discovery and Data Mining

Second Pacific-Asia Conference, PAKDD'98, Melbourne, Australia, April 15-17, 1998, Proceedings

This book constitutes the refereed proceedings of the Second Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD-98, held in Melbourne, Australia, in April 1998. The book presents 30 revised full papers selected from a total of 110 submissions; also included are 20 poster presentations. The papers contribute new results to all current aspects in knowledge discovery and data mining on the research level as well as on the level of systems development. Among the areas covered are machine learning, information systems, the Internet, statistics, knowledge acquisition, data visualization, software reengineering, and knowledge based systems.

This book constitutes the refereed proceedings of the Second Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD-98, held in Melbourne, Australia, in April 1998.

Principles of Data Mining and Knowledge Discovery

Second European Symposium, PKDD'98, Nantes, France, September 23-26, 1998, Proceedings

This book constitutes the refereed proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery, PKDD '98, held in Nantes, France, in September 1998. The volume presents 26 revised papers corresponding to the oral presentations given at the conference; also included are refereed papers corresponding to the 30 poster presentations. These papers were selected from a total of 73 full draft submissions. The papers are organized in topical sections on rule evaluation, visualization, association rules and text mining, KDD process and software, tree construction, sequential and spatial data mining, and attribute selection.

Feature Selection for Knowledge Discovery and Data Mining

With advanced computer technologies and their omnipresent usage, data accumulates in a speed unmatchable by the human's capacity to process data. To meet this growing challenge, the research community of knowledge discovery from databases emerged. The key issue studied by this community is, in layman's terms, to make advantageous use of large stores of data. In order to make raw data useful, it is necessary to represent, process, and extract knowledge for various applications. Feature Selection for Knowledge Discovery and Data Mining offers an overview of the methods developed since the 1970s and provides a general framework in order to examine these methods and categorize them. This book employs simple examples to show the essence of representative feature selection methods and compares them using data sets with combinations of intrinsic properties according to the objective of feature selection. In addition, the book suggests guidelines on how to use different methods under various circumstances and points out new challenges in this exciting area of research. Feature Selection for Knowledge Discovery and Data Mining is intended to be used by researchers in machine learning, data mining, knowledge discovery and databases as a toolbox of relevant tools that help in solving large real-world problems. This book is also intended to serve as a reference book or secondary text for courses on machine learning, data mining, and databases.

As computer power grows and data collection technologies advance, a plethora of data is generated in almost every field where computers are used.

Scalable High Performance Computing for Knowledge Discovery and Data Mining

Scalable High Performance Computing for Knowledge Discovery and Data Mining brings together in one place important contributions and up-to-date research results in this fast moving area. Scalable High Performance Computing for Knowledge Discovery and Data Mining serves as an excellent reference, providing insight into some of the most challenging research issues in the field.

Scalable High Performance Computing for Knowledge Discovery and Data Mining brings together in one place important contributions and up-to-date research results in this fast moving area.

Predictive Data Mining

A Practical Guide

This book presents a unified view of data mining, drawing from statistics, machine learning, and databases and focuses on the preparation of data and the development of an overall problem-solving strategy. It will interest researchers, programmers, and developers in knowledge discovery and data mining in the disciplines of AI, software engineering, and databases.

This book presents a unified view of data mining, drawing from statistics, machine learning, and databases and focuses on the preparation of data and the development of an overall problem-solving strategy.

Data Mining

Technologies, Techniques, Tools, and Trends

Focusing on a data-centric perspective, this book provides a complete overview of data mining: its uses, methods, current technologies, commercial products, and future challenges. Three parts divide Data Mining: Part I describes technologies for data mining - database systems, warehousing, machine learning, visualization, decision support, statistics, parallel processing, and architectural support for data mining Part II presents tools and techniques - getting the data ready, carrying out the mining, pruning the results, evaluating outcomes, defining specific approaches, examining a specific technique based on logic programming, and citing literature and vendors for up-to-date information Part III examines emerging trends - mining distributed and heterogeneous data sources; multimedia data, such as text, images, video; mining data on the World Wide Web; metadata aspects of mining; and privacy issues. This self-contained book also contains two appendices providing exceptional information on technologies, such as data management, and artificial intelligence. Is there a need for mining? Do you have the right tools? Do you have the people to do the work? Do you have sufficient funds allocated to the project? All these answers must be answered before embarking on a project. Data Mining provides singular guidance on appropriate applications for specific techniques as well as thoroughly assesses valuable product information.

CHAPTER 1 INTRODUCTION 1.1 WHAT IS DATA MINING? Data mining is the
process of posing various queries and extracting useful information, patterns, and
trends often previously unknown from large quantities of data possibly stored in ...