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Finding Multiple Coherent Biclusters in Microarray Data Using variable string length Multiobjective Genetic Algorithm

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IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE 2009年第6期13卷 969-975页

作者： Maulik, Ujjwal Mukhopadhyay, Anirban Bandyopadhyay, Sanghamitra Jadavpur Univ Dept Comp Sci & Engn Kolkata 700032 India Univ Kalyani Dept Comp Sci & Engn Kalyani 741235 W Bengal India Indian Stat Inst Machine Intelligence Unit Kolkata 700108 India

Microarray technology enables the simultaneous monitoring of the expression pattern of a huge number of genes across different experimental conditions. Biclustering in microarray data is an important technique that discovers a group of genes that are coregulated in a subset of conditions. Biclustering algorithms require to identify coherent and nontrivial biclusters, i.e., the biclusters should have low mean squared residue and high row variance. A multiobjective genetic biclustering technique is proposed here that optimizes these objectives simultaneously. A novel encoding scheme that uses variable chromosome length is developed. Moreover, a new quantitative measure to evaluate the goodness of the biclusters is proposed. The performance of the proposed algorithm has been evaluated on both simulated and real-life gene expression datasets, and compared with some other well-known biclustering techniques.

关键词： Biclustering mean squared residue (MSR) multiobjective genetic algorithm (GA) row variance variable string length

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A new point symmetry based fuzzy genetic clustering technique for automatic evolution of clusters

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INFORMATION SCIENCES 2009年第19期179卷 3230-3246页

作者： Saha, Sriparna Bandyopadhyay, Sanghamitra Indian Stat Inst Machine Intelligence Unit Kolkata 700108 India

In this paper a fuzzy point symmetry based genetic clustering technique (Fuzzy-VGAPS) is proposed which can automatically determine the number of clusters present in a data set as well as a good fuzzy partitioning of the data. The clusters can be of any size, shape or convexity as long as they possess the property of symmetry. Here the membership values of points to different clusters are computed using the newly proposed point symmetry based distance. A variable number of cluster centers are encoded in the chromosomes. A new fuzzy symmetry based cluster validity index, FSym-index is first proposed here and thereafter it is utilized to measure the fitness of the chromosomes. The proposed index can detect non-convex, as well as convex-non-hyperspherical partitioning with variable number of clusters. It is mathematically justified via its relationship to a well-defined hard cluster validity function: the Dunn's index, for which the condition of uniqueness has already been established. The results of the Fuzzy-VGAPS are compared with those obtained by seven other algorithms including both fuzzy and crisp methods on four artificial and four real-life data sets. Some real-life applications of Fuzzy-VGAPS to automatically cluster the gene expression data as well as segmenting the magnetic resonance brain image with multiple sclerosis lesions are also demonstrated. (C) 2009 Elsevier Inc. All rights reserved.

关键词： Fuzzy clustering Cluster validity index Point symmetry Kd-tree Genetic algorithm variable string length

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A study of some fuzzy cluster validity indices, genetic clustering and application to pixel classification

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FUZZY SETS AND SYSTEMS 2005年第2期155卷 191-214页

作者： Pakhira, MK Bandyopadhyay, S Maulik, U Kalyani Govt Engn Coll Kalyani W Bengal India Indian Stat Inst Machine Intelligence Unit Kolkata 700035 W Bengal India Univ Jadavpur Kolkata W Bengal India

In this article, the effectiveness of variable string length genetic algorithm along with a recently developed fuzzy cluster validity index (PBMF) has been demonstrated for clustering a data set into an unknown number of clusters. The flexibility of a variable string length Genetic Algorithm (VGA) is utilized in conjunction with the fuzzy indices to determine the number of clusters present in a data set as well as a good, fuzzy partition of the data for that number of clusters. A comparative study has been performed for different validity indices, namely, PBMF, XB, PE and PC. The results of the fuzzy VGA algorithm are compared with those obtained by the well known FCM algorithm which is applicable only when the number of clusters is fixed a priori. Moreover, another genetic clustering scheme, that also requires fixing the value of the number of clusters, is implemented. The effectiveness of the PBMF index as the optimization criterion along with a genetic fuzzy partitioning technique is demonstrated on a number of artificial and real data sets including a remote sensing image of the city of Kolkata. (c) 2005 Elsevier B.V. All rights reserved.

关键词： clustering cluster validity indices fuzzy c-means algorithm genetic algorithms pixel classification variable string length

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Pattern classification using genetic algorithms:: Determination of H

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PATTERN RECOGNITION LETTERS 1998年第13期19卷 1171-1181页

作者： Bandyopadhyay, S Murthy, CA Pal, SK Indian Stat Inst Machine Intelligence Unit Calcutta 700035 W Bengal India

A methodology based on the concept of a variable string length GA (VGA) is developed for determining automatically the number of hyperplanes for modeling the class boundaries in a GA-classifier. The genetic operators and fitness function are defined to take care of the variability in chromosome length. It is proved that the method is able to arrive at the optimal number of misclassifications after a sufficiently large number of iterations, and will need a minimal number of hyperplanes for this purpose. Experimental results on different artificial and real life data sets demonstrate that the classifier. using the concept of a variable length chromosome, can automatically determine an appropriate value of the number of hyperplanes, and also provide performance better than that of the fixed length version. Its comparison with another approach using a VGA is provided. (C) 1998 Elsevier Science B.V. All rights reserved.

关键词： genetic algorithms optimum hyperplane fitting speech recognition variable string length

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Application of a Multiseed-Based Clustering Technique for Automatic Satellite Image Segmentation

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2010年第2期7卷 306-308页

作者： Saha, Sriparna Bandyopadhyay, Sanghamitra Indian Stat Inst Machine Intelligence Unit Kolkata 700108 India

The problem of classifying an image into different homogeneous regions is viewed as a task of clustering the pixels in the intensity space. In this letter, a newly developed genetic clustering technique is used for automatically segmenting remote sensing satellite images. Each cluster is divided into several small hyperspherical subclusters, and the centers of all these small subclusters are encoded in a chromosome to represent the whole clustering. For assigning points to different clusters, these local subclusters are considered individually. For the purpose of objective function evaluation, these subclusters are merged appropriately to form a variable number of global clusters. A newly proposed point-symmetry-distance-based cluster validity index, Sym index, is used as a measure of the validity of the corresponding segment. The effectiveness of the proposed technique compared to a fuzzy C-means clustering technique, a recently proposed GAPS clustering with Sym-index-based method, and a subtractive clustering technique is demonstrated in identifying different land cover regions from two numeric image data sets and a remote sensing image of a part of the city of Kolkata.

关键词： Genetic algorithm point-symmetry-based distance remote sensing imagery variable string length

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Multiobjective Fuzzy Biclustering in Microarray Data: Method and a New Performance Measure

Multiobjective Fuzzy Biclustering in Microarray Data: Method...

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IEEE Congress on Evolutionary Computation

作者： Maulik, Ujjwal Mukhopadhyay, Anirban Bandyopadhyay, Sanghamitra Zhang, Michael Q. Zhang, Xuegong Jadavpur Univ Dept Comp Sci & Engn Kolkata 700032 India Univ Kalyani Dept Comp Sci & Engn Kalyani 741235 W Bengal India Indian Stat Inst Machine Inst Unit Kolkata 700108 India Cold Spring Harbor Lab Huntington NY USA Tisnghua Univ Dept Automat Beijing Peoples R China

ISBN: (纸本)9781424418220

Objective of any biclustering algorithm in microarray data is to discover a subset of genes that are expressed similarly in a subset of conditions. The boundaries of biclusters usually overlap as genes and conditions may belong to different biclusters with different membership degrees. Hence the notion of fuzzy sets is useful for discovering such overlapping biclusters. In this article an attempt has been made to develop a multiobjective genetic algorithm based approach for probabilistic fuzzy biclustering that minimizes the residual and maximizes cluster size and expression profile variance. A novel variable string length encoding has been proposed in this regard that encodes multiple biclusters in a single string. Also a new performance measure that reflects how a bicluster is statistically distinguished from the background is proposed. Performance of the proposed algorithm has been compared with some well known biclustering algorithms.

关键词： Fuzzy biclustering fuzzy K-medoids mean squared residue expression profile variance multiobjective genetic algorithm variable string length statistical difference from background

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