KDnuggets : News : 2000 : n03 : item19


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Date: Sat, 5 Feb 2000 08:55:53 -0800
From: Frank Lemke frank@knowledgeminer.net
Subject: Book: Self-organising Data Mining and KnowledgeMiner Software
We would like to announce the availability of our new book:

 "Self-organising Data Mining" by Johann-Adolf Mueller and Frank Lemke

From the Preface:
 This book is dedicated to
 Prof. A.G. Ivakhnenko,
 the father of GMDH,
 to his eighty fifth' birthday

This book covers several areas of knowledge discovery and describes a
spectrum of parametric and nonparametric self-organizing modeling methods.
It introduces principles of evolution - inheritance, mutation and selection -
for generating a network structure systematically enabling automatic model structure
synthesis and model validation. Models are generated from the data in the form of
networks of active neurons in an evolutionary fashion of repetitive generation of
populations of competing models of growing complexity and their validation and
selection until an optimal complex model - not too simple and not too complex - has
been created. Neither, the number of neurons and the number of layers in the
network, nor the actual behavior of each created neuron is predefined. All this
is adjusted during the process of self-organisation, and therefore, is called
self-organising data mining.

A self-organising data mining creates optimal complex models systematically and
autonomously by employing both parameter and structure identification. An
optimal complex model is a model that optimally balances model quality on a given
learning data set ("closeness of fit") and its generalisation power on new, not
previously seen data with respect to the data's noise level and the task of modelling
(prediction, classification, modelling, etc.). It thus solves the basic problem of
experimental systems analysis of systematically avoiding "overfitted" models based
on the data's information only. This makes self-organising data mining a most
automated, fast and very efficient supplement and alternative to other data mining

The differences between Neural Networks and this new approach focus on
Statistical Learning Networks and induction. The first Statistical Learning Network
algorithm of this new type, the Group Method of Data Handling (GMDH), was
developed by A.G. Ivakhnenko in 1967. Considerable improvements were
introduced in the 1970s and 1980s by versions of the Polynomial Network Training
algorithm (PNETTR) by Barron and the Algorithm for Synthesis of Polynomial
Networks (ASPN) by Elder when Adaptive Learning Networks and GMDH were
flowing together. Further enhancements of the GMDH algorithm have been realized
in the "KnowledgeMiner" software described and enclosed in this book.

The book spans eight chapters. Each chapter includes some practical examples
and a reference list for further reading. The areas covered are:

                1 Knowledge Discovery from Data

                2 Self-organising Data Mining

                3 Self-organising Modelling Technologies
                      Statistical Learning Networks
                      Inductive approach

                4 Parametric GMDH Algorithms

                5 Nonparametric Algorithms
                      Cluster Analysis
                      Analog Complexing
                      Self-organising Fuzzy Rule Induction

                6 Application of Self-organising Data Mining
                      Spectrum of self-organising data mining methods

                7 KnowledgeMiner

                8 Sample Applications
                      national economy
                      balance sheet
                      sales prediction
                      energy consumption
                      water pollution
                      heart disease
                      U.S. congressional voting behavior

More information and a downloadable PDF edition of the book is
available at:


Thank you for your attention.

Frank Lemke

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KDnuggets : News : 2000 : n03 : item19

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