Publications

Announcing publication of the book by Guenther Gediga and Ivo Duentsch

"Rough set data analysis - A road to non-invasive knowledge discovery"
Methodos Publishers (UK), 2000, ISBN 190328001X, 107pp, GBP 10.00, Euro 15.00

It can be ordered in any bookstore or directly from the publisher's web page

http://www.methodos.eu.com

where more details can be found.

Regards,
Ivo Duentsch

Abstract:

This is not the first book on rough set analysis and certainly not the first
book on knowledge discovery algorithms, but it is the first attempt to do
this in a non-invasive way.

The term "non-invasive" in connection with knowledge discovery or data
analysis is new and needs some introductory remarks. We have worked from
about 1993 on topics of knowledge discovery and/or data analysis (both topics
are sometimes hard to distinguish), and we felt that most of the common work
on this topics was based on at least discussable assumptions. We regarded the
invention of Rough Set Data Analysis (RSDA) as one of the big events in those
days, because, at the start, RSDA was clearly structured, simple, and
straightforward from basic principles to effective data analysis. It is our
conviction that a model builder who uses a structural and/or statistical
system should be clear about the basic assumptions of the model. Furthermore,
it seems to be a wise strategy to use models with only a few
(pre-)assumptions about the data. If both characteristics are fulfilled, we
call a modelling process non-invasive. This idea is not really new, because
the non-parametric statistics approach based on the motto of R.A.Fisher

�������� ������� Let the data speak for themselves,

can be transferred to the context of knowledge discovery. It is no wonder
that e.g. the randomisation procedure (one of the flagships of non-parametric
statistics) is part of the non-invasive knowledge discovery approach.

In this book we present an overview of the work we have done in the past
seven years on the foundations and details of data analysis. During this
time, we have learned to look at data analysis from many different angles,
and we have tried not to be biased for - or against - any particular method,
although our ideas take a prominent part of this book. In addition, we have
included many citations of papers on RSDA in knowledge discovery by other
research groups as well to somewhat alleviate the emphasis on our own work.
We hope that the presentation is neither too rough nor too fuzzy, so that the
reader can discover some knowledge in this book

Contents:

1. Introduction �
2. Data models and model assumptions �
3. Basic rough set data analysis �
3.1 Fundamentals �
3.2 Approximation quality �
3.3 Information systems �I
3.4 Indiscernability relations �
3.5 Feature selection �
3.6 Discernability matrices and Boolean reasoning �
3.7 Rules �
3.8 Approximation quality of attribute sets
4. Rule significance �
4.1 Significant and casual rules �
4.2 Conditional significance �
4.3 Sequential randomisation
5. Data discretisation �
5.1 Classificatory discretisation �
5.2 Discretisation of real valued attributes
6. Model selection �
6.1 Dynamic reducts �
6.2 Rough entropy measures �
6.3 Entropy measures and approximation quality
7. Probabilistic granule analysis �
7.1 The variable precision model �
7.2 Replicated decision systems �
7.3 An algorithm to find probabilistic rules �
7.4 Unsupervised learning and nonparametric distribution estimates
8. Imputation �
8.1 Statistical procedures �
8.2 Imputation from known values
9.0 Beyond rough sets �
9.1 Relational attribute systems
9.2 Non-invasive test theory
10. Epilogue