Миркин Борис Григорьевич

Developing Additive Spectral Approach to Fuzzy Clustering

2011 · ARTICLE · en

An additive spectral method for fuzzy clustering is presented. The method operates on a clustering model which is an extension of the spectral decomposition of a square matrix. The computation proceeds by extracting clusters one by one, which allows us to draw several stoppingrules to the procedure. We experimentally test the performance of our method and show its competitiveness.method and show its competitiveness.

How to visualize a crisp or fuzzy topic set over a taxonomy

2011 · ARTICLE · en

A Hybrid Cluster-Lift Method for the Analysis of Research Activities

2010 · ARTICLE · en

Гибрид двух новых методов, спектральной кластеризации и метод таксономии - применяется для анализа научно-исследовательской деятельности кафедры. Приведенн пример, иллюстрирующий этот метод, который применяется для решения реальных задач.

Choosing a discernibility measure for reject-option of individual and multiple classifiers

2010 · ARTICLE · en

Предложен новый метод оценки надежности классификации, основанный на различимости класса модели.

Intelligent Choice of the Number of Clusters in K-Means Clustering: An Experimental Study with Different Cluster Spreads

2010 · ARTICLE · en

Интеллектуальный выбор количества кластеров в методе кластеризации k-средних: экспериментальное исследование с различными кластерными спредами.

Similarity Clustering of Proteins Using Substantive Knowledge and Reconstruction of Evolutionary Gene Histories in Herpesvirus

2010 · ARTICLE · en

The issue of clustering proteins into homologous protein families (HPFs) has attracted considerable attention by researchers. On one side, many databases of protein families have been developed by using popular sequence alignment tools and relatively simple clustering methods followed by extensive manual curation. On the other side, more elaborate clustering approaches have been used, yet with a very limited degree of success. This paper advocates an approach to clustering protein families involving knowledge of the protein functions to adjust the parameter of similarity scale shift. One more source of external information is utilised as we proceed to reconstruct HPF evolutionary histories over an evolutionary tree; the consistency between these histories and information on gene arrangement in the genomes is used to narrow down the choice of the clustering.

Cluster-lift method for mapping research activities over a concept tree

2009 · CHAPTER · en

The paper builds on the idea by R. Michalski of inferential concept interpretationfor knowledge transmutation within a knowledge structure taken here tobe a concept tree. We present a method for representing research activities within aresearch organization by doubly generalizing them. To be specific, we concentrateon the Computer Sciences area represented by the ACM Computing ClassificationSystem (ACM-CCS). Our cluster-lift method involves two generalization steps: oneon the level of individual activities (clustering) and the other on the concept structurelevel (lifting). Clusters are extracted from the data on similarity between ACMCCStopics according to the working in the organization. Lifting leads to conceptualgeneralization of the clusters in terms of “head subjects” on the upper levels ofACM-CCS accompanied by their gaps and offshoots. A real-world example of therepresentation is provided.

A measure of domain of applicability for QSAR modelling based on intelligent K-Means clustering

2007 · ARTICLE · en

A suffix tree approach to anti-spam email filtering

2006 · ARTICLE · en

We present an approach to email filtering based on the suffix tree data structure. A method for the scoring of emails using the suffix tree is developed and a number of scoring and score normalisation functions are tested. Our results show that the character level representation of emails and classes facilitated by the suffix tree can significantly improve classification accuracy when compared with the currently popular methods, such as naive Bayes.We believe the method can be extended to the classification of documents in other domains.

Cluster Analysis for Researchers: A Book Review

2005 · ARTICLE · en