Cutting-edge data mining techniques and tools for solving your toughest analytical problems Data Mining Solutions In down-to-earth language, data mining experts Christopher Westphal and Teresa Blaxton introduce a brand new approach to data mining analysis. Through their extensive real-world experience, they have developed and documented many practical and proven techniques to make your own data mining efforts more successful. You'll get a refreshing "out-of-the-box" approach to data mining that will help you maximize your time and problem-solving resources, and prepare for the next wave of data mining-visualization. You will read about ways in which data mining has been used to: * Discover patterns of insider trading in the stock market * Evaluate the utility of marketing campaigns * Analyze retail sales patterns across geographic regions * Identify money laundering operations * Target DNA sequences for pharmaceutical testing and development The book is accompanied by a CD-ROM that contains: * Demo and trial versions of numerous visual data mining tools * Active web-page links for each of the products profiled * GIF files corresponding to all book images
Unfortunately the field of data mining will be no different. Already we are
bombarded with a host of new buzzwords that sound great, but in reality are old
ideas being touted as part of the new data mining technology. Let us state from
the outset ...
Illustrating recent advances in data mining problems, encompassing both original research results and practical development experiences, this book features the proceedings of the First International Conference on Data Mining. Contributions from academia and industry, covering such diverse areas as machine learning, databases, statistics, knowledge acquisition, data visualization and knowledge-based systems are included.
One requirement of data mining is efficiency and scalability of mining algorithms.
It makes the use of parallelism even more relevant to provide a way of processing
long running tasks in a timely manner. In this context, parallel database ...
This book contains all the practical information, hands-on demos and software you need to understand data mining.This book doesn't just explain data mining concepts: it shows you exactly how to make the most of them. If you're in marketing, you'll learn how data mining can help you rank your customers by the likelihood they'll respond to your mailings. If you're in MIS, you'll learn exactly how to prepare relational data for data mining. You'll learn how to use each of three powerful data mining tools; demos for all three are included on CD-ROM. The book also includes detailed case studies for several of the industries that can benefit most from data mining, including banking, finance, retail, healthcare, direct marketing, and telecommunications. The book is replete with shortcuts and techniques that have never been published before.For all business and marketing professionals, systems analysts, database administrators, students and others who want to leverage the power of data mining.
Another paper discusses eight kinds of problems solved by data mining and
differentiates data mining vendors by the problems that each vendor tackles. A
reader looking for standards in the area of data mining will be sadly disappointed
.
Through extensive case studies and examples, this book provides practical guidance on all aspects of implementing data mining: technical, business, and social. The book also demonstrates IBM's powerful new intelligent Miner tool and shows how it can be applied.
Part 2, Discovery Part 2 is for readers who want a good understanding of what
actually happens during data mining, how the algorithms work, and how to
assess vendor solutions for data mining. > Chapter 3, The Data Mining Process
This ...
Today's database engineers are committed to the reuse of data, for performance and economic reasons. Moreover, they often have to complement enterprise data with data from external sources, where the corresponding semantics is rarely fully available. As a consequence, both database researchers and practitioners are facing salient issues in the discovery and understanding of the semantics hidden in various forms of external sources: data sets, data stores formats, database schemes, application programs, documentation etc. This book focuses on such issues from the perspective of database semantics, discussing theories, principles and models for recovering, representing and organizing semantic information on application data.
4.6 Backtracking and comparative mining analysis Backtracking is convenient for
OLAP mining since a user may like to tentatively dig deep following some mining
paths and later try alternatives if there have not been desired interesting ...
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.
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.
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.
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.
