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https://ceur-ws.org/Vol-758/Preface.pdf
Alexey N. Averkin, Dmitry I. Ignatov, Sushmita Mitra, Jonas Poelmans,
Valery B. Tarasov (Eds.)
SKAD’11 – Soft Computing Applications and Knowledge
Discovery
Workshop co-located with the 13th International Conference on Rough Sets,
Fuzzy Sets, Data Mining, and Granular Computing (RSFDGrC-2011) and the
4th International Conference on Pattern Recognition and Machine Intelligence
(PReMI-2011)
June 2011, Moscow, Russia
The proceedings are published online in the CEUR-Workshop series (ISSN 1613-
0073) and the volume Vol-758 has a unique URN: urn:nbn:de:0074-758-4.
i
Volume Editors
Alexey N. Averkin
Dorodnicyn Computing Centre of the Russian Academy of Sciences, Russia
Dmitry I. Ignatov
School of Applied Mathematics and Information Science
National Research University Higher School of Economics, Moscow, Russia
Sushmita Mitra
Machine Intelligence Unit
Indian Statistical Institute, Kolkata, India
Jonas Poelmans
Faculty of Business and Economics
Katholieke Universiteit Leuven, Belgium
Valery B. Tarasov
Bauman Moscow State Technical University, Russia
Copyright c 2011 for the individual papers by papers’ authors, for the Volume
by the editors. All rights reserved. No part of this publication may be reproduced,
stored in a retrieval system, or transmitted, in any form or by any means without
the prior permission of the copyright owners.
ii
Preface
Soft computing is a collection of methodologies, which aim to exploit tolerance
for imprecision, uncertainty and partial truth to achieve tractability, robustness
and low cost solution in real life tasks. This volume contains the papers presented
at SCAKD-2011: The International Workshop on Soft Computing Applications
and Knowledge Discovery held on June 25, 2011 in Moscow. This workshop was
initiated with the aim of presenting high quality scientific results and promising
research in the areas of soft computing and data mining, particularly by young
researchers, with an objective of bringing them to the focus while promoting
collaborative research activities. The main goal of this workshop was to gather
researchers all areas of Soft Computing Applications and Knowledge Discovery,
including but not limited to the following: Pattern Recognition, Data Mining &
Knowledge Discovery, Fuzzy & Neural Networks, Evolutionary & Probabilistic
Computing, Swarm Intelligence, Collective Intelligence, Machine Learning, In-
formation Retrieval, Rough Sets, Soft Computing, Bio-informatics, Biometrics,
Computational Biology, Clustering, Formal Concept Analysis, Ontology Learn-
ing, Decision Support Systems & Business Intelligence (OLAP and BI, Data
Warehouse Modeling, ETL techniques and technologies, and Data Visualiza-
tion), Recommender Systems, Modeling of user behavior, and Applications of
Soft Computing.
By holding the workshop in conjunction with PReMI and RSFDGrC, we hope
to provide the contributers exposure and interaction with eminent scientists,
engineers, professionals, and researchers in related fields. We are proud that
in total, 15 papers were accepted for oral presentation and publication in the
proceedings. Finally we would like to say a word of thank to the administration
of the Higher School of Economics who took care of all arrangements to make
this conference pleasant and enjoyable.
June, 2011 Alexey N. Averkin
Moscow Dmitry I. Ignatov
Sushmita Mitra
Jonas Poelmans
Valery B. Tarasov
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Organization
This SCAKD’11 workshop was held in June 2011 in Moscow, Russia co-located
with the 13th International Conference on Rough Sets, Fuzzy Sets, Data Mining,
and Granular Computing (RSFDGrC-2011) and 4th International Conference
on Pattern Recognition and Machine Intelligence (PReMI-2011) at the National
Research University Higher School of Economics.
Program Chairs
Alexey N. Averkin Dorodnicyn Computing Centre of the Russian
Academy of Sciences, Russia
Dmitry I. Ignatov State University Higher School of Economics, Russia
Sushmita Mitra Indian Statistical Institute, India
Jonas Poelmans Katholieke Universiteit Leuven, Belgium
Valery B. Tarasov Bauman Moscow State Technical University, Russia
Program Committee
Mehdi Kaytoue, France
Yuri Kudryavtsev, Russia
Sergei Kuznetsov, Russia
Xenia Naidenova, Russia
Andrey Savchenko, Russia
Dominik Slezak, Poland
Laszlo Szathmary, Canada
Rustam Tagiew, Germany
Sponsoring Institutions
ABBYY, Moscow
Russian Foundation for Basic Research, Moscow
Poncelet Laboratory (UMI 2615 du CNRS), Moscow
State University Higher School of Economics, Moscow
Yandex, Moscow
Witology, Moscow
Dynasty Foundation, Moscow
Table of Contents
A New Method of DDB Logical Structure Synthesis Using Distributed
Tabu Search . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Eduard Babkin and Margarita Karpunina
Service Centers Finding by Fuzzy Antibases of Fuzzy Graph . . . . . . . . . . . . 12
Leonid Bershtein, Alexander Bozhenyuk and Igor Rosenberg
Forecasting the U.S. stock market via Levenberg-Marquardt
and Herman Haken artificial neural networks using ICA&PCA
pre-processing techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
Sergey Golovachev
Estimating Probability of Failure of a Complex System Based on
Partial Information about Subsystems and Components, with Potential
Applications to Aircraft Maintenance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
Christelle Jacob, Didier Dubois, Janette Cardoso, Martine Ceberio and
Vladik Kreinovich
Stepwise Feature Selection Using Multiple Kernel Learning . . . . . . . . . . . . . 42
Vilen Jumutc
Empirical reconstruction of fuzzy model of experiment in the Euclidean
metric . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
Tatiana Kopit
SVM Based Offline Handwritten Gurmukhi Character Recognition . . . . . . 51
Munish Kumar, M. K. Jindal and R. K. Sharma
Obtaining of a Minimal Polygonal Representation of a Curve by Means
of a Fuzzy Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
Alexander Lepskiy
KDDClus: A Simple Method for Multi-Density Clustering . . . . . . . . . . . . . . 72
Sushmita Mitra and Jay Nandy
Intelligent Data Mining for Turbo-Generator Predictive Maintenance:
An Approach in Real-World . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
Alexandre Pellicel, Gonçalo Cassio, Marco Aurelio Lopes, Luiz Eduardo
Borges Da Silva, Erik Bonaldi, Levy Ely Lacerda De Oliveira, Jonas
Borges Da Silva, Germano Lambert-Torres and Pierre Rodrigues
Fuzzy Predicting Models in ”Structure - Property” Problem . . . . . . . . . . . . 89
Eugeny Prokhorov, Ludmila Ponomareva, Eugeny Permyakov and Mikhail
Kumskov
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Handwritten Script Identification from a Bi-Script Document at Line
Level using Gabor Filters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
Ganapatsingh Rajput and Anita H. B.
Image Recognition Using Kullback-Leibler Information Discrimination . . . 102
Andrey Savchenko
Beyond Analytical Modeling, Gathering Data to Predict Real Agents’
Strategic Interaction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113
Rustam Tagiew
Construction of Enzyme Network of Arabidopsis thaliana using graph
theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125
Kasthuribai Viswanathan and Nita Parekh
vi