Data Mining and Knowledge Discovery

Data Mining and Knowledge Discovery

Andi Baritchi (Corporate Data Systems, USA)
DOI: 10.4018/978-1-59140-206-0.ch003
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In today’s business world, the use of computers for everyday business processes and data recording has become virtually ubiquitous. With the advent of this electronic age comes one priceless by-product — data. As more and more executives are discovering each day, companies can harness data to gain valuable insights into their customer base. Data mining is the process used to take these immense streams of data and reduce them to useful knowledge. Data mining has limitless applications, including sales and marketing, customer support, knowledge-base development, not to mention fraud detection for virtually any field, etc. “Data mining,” a bit of a misnomer, refers to mining the data to find the gems hidden inside the data, and as such it is the most often-used reference to this process. It is important to note, however, that data mining is only one part of the Knowledge Discovery in Databases process, albeit it is the workhorse. In this chapter, we provide a concise description of the Knowledge Discovery process, from domain analysis and data selection, to data preprocessing and transformation, to the data mining itself, and finally the interpretation and evaluation of the results as applied to the domain. We describe the different flavors of data mining, including association rules, classification and prediction, clustering and outlier analysis, customer profiling, and how each of these can be used in practice to improve a business’ understanding of its customers. We introduce the reader to some of today’s hot data mining resources, and then for those that are interested, at the end of the chapter we provide a concise technical overview of how each data-mining technology works.

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Table of Contents
Thomas L. Hill
Mahesh S. Raisinghani
Mahesh S. Raisinghani
Chapter 1
Clare Brindley, Bob Ritchie
This chapter proposes that the initial perceptions of uncertainty and risk relating to decision making are unlikely to be modified irrespective of... Sample PDF
Reducing Risk in Information Search Activities
Chapter 2
Mahesh Raisinghani, John H. Nugent
This chapter presents a high-level model for employing intelligent agents in business management processes, much like has been successfully... Sample PDF
Intelligent Agents for Competitive Advantage: Requirements and Issues
Chapter 3
Andi Baritchi
In today’s business world, the use of computers for everyday business processes and data recording has become virtually ubiquitous. With the advent... Sample PDF
Data Mining and Knowledge Discovery
Chapter 4
Ulfert Gartz
Although capacity and functionality of information management systems increased remarkably in the last years, the information and knowledge supply... Sample PDF
Enterprise Information Management
Chapter 5
Rahul Singh, Lakshmi Iyer, Al Salam
This chapter presents an Intelligent Knowledge-Based Multi-Agent Architecture for Collaboration (IKMAC) in B2B e-Marketplaces. IKMAC is built upon... Sample PDF
An Intelligent Knowledge-Based Multi-Agent Architecture for Collaboration (IKMAC) in B2B e-Marketplaces
Chapter 6
Dan Sullivan
As the demand for more effective Business Intelligence (BI) techniques increases, BI practitioners find they must expand the scope of their data to... Sample PDF
Text Mining in Business Intelligence
Chapter 7
James E. Skibo
This chapter describes both the nature of trade allowances and the unique approach taken by one major retailer in overcoming legacy system obstacles... Sample PDF
Bypassing Legacy Systems Obstacles: How One Company Built Its Intelligence to Identify and Collect Trade Allowances
Chapter 8
Edilberto Casado
This chapter explores the opportunities to expand the forecasting and business understanding capabilities of Business Intelligence (BI) tools with... Sample PDF
Expanding Business Intelligence Power with System Dynamics
Chapter 9
Jeffrey Hsu
Most businesses generate, are surrounded by, and are even overwhelmed by data — much of it never used to its full potential for gaining insights... Sample PDF
Data Mining and Business Intelligence: Tools, Technologies, and Applications
Chapter 10
Somya Chaudhary
This chapter focuses on the factors necessary for strategic Business Intelligence (BI) success from a managerial point of view. BI results from the... Sample PDF
Management Factors for Strategic BI Success
Chapter 11
Hércules Antonio do Prado, José Palazzo Moreira de Oliveira, Edilson Ferneda, Leandro Krug Wives, Edilberto Magalhaes, Stanley Loh
Business Intelligence (BI) can benefit greatly from the bulk of knowledge that stays hidden in the large amount of textual information existing in... Sample PDF
Transforming Textual Patterns into Knowledge
Chapter 12
John D. Wells, Traci J. Hess
Many businesses have made or are making significant investments in data warehouses that reportedly support a myriad of decision support systems... Sample PDF
Understanding Decision-Making in Data Warehousing and Related Decision Support Systems: An Explanatory Study of a Customer Relationship Management Application
Chapter 13
Hamid R. Nemati, Christopher D. Barko, Ashfaaq Moosa
Electronic Customer Relationship Management (e-CRM) Analytics is the process of analyzing and reporting online customer/visitor behavior patterns... Sample PDF
E-CRM Analytics: The Role of Data Integration
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