PME FACE AU DEFI DE L'INTELLIGENCE ECONOMIQUE

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Collection : Stratégies et
1-55860-478-2 Note: If you already own Predictive Data Mining: A
Practical
Guide, please click here to order the software only. To
order the book
without software, please click here.

The potential business advantages of data mining are
well documented
in publications for executives and managers. However,
developers
implementing major data-mining systems need concrete
information
about the underlying technical principles_and their
practical
manifestations_in order to either integrate
commercially available tools
or write data-mining programs from scratch. This book
is the first
technical guide to provide a complete, generalized
roadmap for
developing data-mining applications, together with
advice on
performing these large-scale, open-ended analyses for
real-world data
warehouses.

Focuses on the preparation and organization of
data and the
development of an overall strategy for data
mining.
Reviews sophisticated prediction methods that
search for
patterns in big data.
Describes how to accurately estimate future
performance of
proposed solutions.
Illustrates the data-mining process and its
potential pitfalls
through real-life case studies.

A state-of-the-art data-mining software kit accompanies
the book. The
software, which is delivered through a special web
site, is a collection
of routines for efficient mining of big data. Both
classical and the more
computationally expensive state-of-the-art prediction
methods are
included. Using a standard spreadsheet data format,
this kit implements
all of the data-mining tasks described in the book. The
software is
available for Windows 95/NT and Unix platforms (no need
to specify
when ordering). For more information, please visit
http://www.data-miner.com.

"I enjoy reading PREDICTIVE DATA MINING. It
presents an excellent perspective on the theory
and
practice of data mining. It can help educate
statisticians to build alliances between
statisticians
and data miners."

Emanuel Parzen
Distinguished Professor of Statistics, Texas A&M
University

Authors:

Sholom M. Weiss is a professor of computer science at
Rutgers
University and the author of dozens of research papers
on data mining
and knowledge-based systems. He is a fellow of the
American
Association for Artificial Intelligence, serves on
numerous editorial
boards of scientific journals, and has consulted widely
on the
commercial application of advanced data mining
techniques. He is the
author, with Casimir Kulikowski, of Computer Systems
That Learn:
Classification and Prediction Methods from Statistics,
Neural
Nets, Machine Learning, and Expert Systems, which is
also available
from Morgan Kaufmann Publishers.

Nitin Indurkhya is on the faculty at the Basser
Department of Computer
Science, University of Sydney, Australia. He has
published extensively
on Data Mining and Machine Learning and has
considerable
experience with industrial data-mining applications in
Australia, Japan
and the USA.

Table of Contents:


1 What is Data Mining?
2 Statistical Evaluation for Big Data
3 Preparing the Data
4 Data Reduction
5 Looking for Solutions
6 What's Best for Data Reduction and Mining?
7 Art or Science? Case Studies in Data Mining

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