Date: Sat, 5 Feb 2000 08:55:53 -0800
From: Frank Lemke email@example.com
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 methods. 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 a.o. More information and a downloadable PDF edition of the book is available at: http://www.knowledgeminer.net Thank you for your attention. Frank Lemke
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