ANALYSIS & COMMENTARY
REAL WORLD EXPERIENCES WITH
DATA MINING AND KNOWLEDGE DISCOVERY: PART I
by Jill Dyche & Evan Levy
Jill Dyche and Evan Levy are Partners at Baseline Consulting Group, a consulting firm specializing in data management and database marketing consulting to Fortune 500 companies worldwide.
In this first installment of a three-part series, they write: "These days, there are as many different definitions of data mining as there are tools in the marketplace. Vendors of such diverse technologies as OLAP, Knowledge Discovery, behavior modeling, customer segmentation software, and affinity analysis are all taking shelter under the data mining umbrella. The byproduct of this all-for-one mentality is general confusion in the marketplace."
WHEN SHOULD WE BE AFRAID OF DATA MINING?
by Michael J.A. Berry
Michael J. A. Berry is a client partner and director of the data mining center at Naviant Technology Solutions. Along with Gordon Linoff, also of Naviant, he is author of Data Mining Techniques for Marketing, Sales and Customer Support (1997, John Wiley & Sons). For more information on the book, see http://www.data-miners.com For more information on Naviant Technology Solutions, see http://www.naviant.com
Berry comments: "In an earlier column...I tried to justify my lack of concern at having personal data such as my shopping habits, magazine subscriptions, web site visits and even telephone calls mined and analyzed by enterprising database marketers...I ended that column with a promise that in a later column I would explore the circumstances where data mining really does pose a threat. This is that column."
DECISION TECHNOLOGIES IN DATABASE MARKETING: PART VII
by Gene M. Ferruzza, Senior VP, Decision Technologies
For 14 years, Gene Ferruzza has provided integrated business solutions for clients in telecommunications, electric utilities, financial services, aerospace, manufacturing, and retail. He is an internationally recognized expert in strategic database marketing planning and implementation, as well as development and application of data marts, statistical and A.I. modeling, and decision systems for understanding and predicting human behavior. In addition, he directs research in statistical, neural, evolutionary, and hybrid modeling techniques, and the implementation of decision technologies for marketing programs and software productization. He has also developed and marketed his own database management and segmentation software and is currently advising in on-line market research services and products. Prior to CMS, he worked as a consultant and instructor for two leading neural network hardware and software providers (HNC and NeuralWare). Gene graduated from the University of Pittsburgh with a B.S. in Computer Science and Mathematics.
In this seventh installment of an extensive multi-part series Feruzza writes: "Non-parametric modeling techniques also are data-driven. In contrast to parametric models, non-parametric models require no assumptions about the functional form of the relationship between the target behavior (dependent variable) and the independent variables. In cases where it is difficult to fit the data to particular form, non-parametric techniques offer more flexibility. In other words, they have the ability conform their structure to that of the best solution."
Parts I through VI of this series are available as D S * articles 100073, 100080, 100085, 100091, 100097 & 100103.
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