Sumathi / Sivanandam Introduction to Data Mining and its Applications

1. Auflage 2006
ISBN: 978-3-540-34351-6
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 29, 851 Seiten, eBook

Reihe: Studies in Computational Intelligence

This book explores the concepts of data mining and data warehousing, a promising and flourishing frontier in data base systems and new data base applications and is also designed to give a broad, yet in-depth overview of the field of data mining. Data mining is a multidisciplinary field, drawing work from areas including database technology, AI, machine learning, NN, statistics, pattern recognition, knowledge based systems, knowledge acquisition, information retrieval, high performance computing and data visualization. This book is intended for a wide audience of readers who are not necessarily experts in data warehousing and data mining, but are interested in receiving a general introduction to these areas and their many practical applications. Since data mining technology has become a hot topic not only among academic students but also for decision makers, it provides valuable hidden business and scientific intelligence from a large amount of historical data. It is also written for technical managers and executives as well as for technologists interested in learning about data mining.

Zielgruppe

Research

Autoren/Hrsg.

Sumathi, S.

Sivanandam, S.N.

Inhaltsverzeichnis

to Data Mining Principles.- Data Warehousing, Data Mining, and OLAP.- Data Marts and Data Warehouse.- Evolution and Scaling of Data Mining Algorithms.- Emerging Trends and Applications of Data Mining.- Data Mining Trends and Knowledge Discovery.- Data Mining Tasks, Techniques, and Applications.- Data Mining: an Introduction – Case Study.- Data Mining & KDD.- Statistical Themes and Lessons for Data Mining.- Theoretical Frameworks for Data Mining.- Major and Privacy Issues in Data Mining and Knowledge Discovery.- Active Data Mining.- Decomposition in Data Mining - A Case Study.- Data Mining System Products and Research Prototypes.- Data Mining in Customer Value and Customer Relationship Management.- Data Mining in Business.- Data Mining in Sales Marketing and Finance.- Banking and Commercial Applications.- Data Mining for Insurance.- Data Mining in Biomedicine and Science.- Text and Web Mining.- Data Mining in Information Analysis and Delivery.- Data Mining in Telecommunications and Control.- Data Mining in Security.

Leseproben

2 Data Warehousing, Data Mining, and OLAP (p. 21)

Objectives:

• This deals with the concept of data mining, need and opportunities, trends and challenges, data mining process, common and new applications of data mining, data warehousing, and OLAP concepts.

• It gives an introduction to data mining: what it is, why it is important, and how it can be used to provide increased understanding of critical relationships in rapidly expanding corporate data warehouse.

• Data mining and knowledge discovery are emerging as a new discipline with important applications in science, engineering, health care, education, and business.

• New disciplined approaches to data warehousing and mining are emerging as part of the vertical solutions approach.

• Extracting the information and knowledge in the form of new relationships, patterns, or clusters for decision making purposes.

• We briefly describe some success stories involving data mining and knowledge discovery.

• We describe five external trends that promise to have a fundamental impact on data mining.

• The research challenges are divided into five broad areas: A) improving the scalability of data mining algorithms, B) mining nonvector data, C) mining distributed data, D) improving the ease of use of the data mining systems and environments, and E) privacy and security issues for data mining.

• We present the concept of data mining and aim at providing an understanding of the overall process and tools involved: how the process turns out, what can be done with it, what are the main techniques behind it, and which are the operational aspects.

• OLAP servers logically organize data in multiple dimensions, which allows users to quickly and easily analyze complex data relationships.

• OLAP database servers support common analytical operations, including consolidation, drill-down, and slicing and dicing.

• OLAP servers are very eficient when storing and processing multidimensional data.

Abstract.

This deals with the concept of data mining, need and opportunities, trends and challenges, process, common and new applications, data warehousing, and OLAP concepts. Data mining is also a promising computational paradigm that enhances traditional approaches to discovery and increases the opportunities for breakthroughs in the understanding of complex physical and biological systems.

Researchers from many intellectual communities have much to contribute to this field. Data mining refers to the act of extracting patterns or models from data. The rate growth of disk storage and the gap between Moore’s law and storage law growth trends represent a very interesting pattern in the state of technology evolution. The ability to capture and store data has produced a phenomenon we call the data tombs or data stores that are effectively write-only.

"Data Mining" (DM) is a folkloric denomination of a complex activity, which aims at extracting synthesized and previously unknown information from large databases. It also denotes a multidisciplinary field of research and development of algorithms and software environments to support this activity in the context of real-life problems where often huge amounts of data are available for mining.