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Sriparna Saha - One of the best experts on this subject based on the ideXlab platform.
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A line symmetry based genetic Clustering Technique: encoding lines in chromosomes
International Journal of Machine Learning and Cybernetics, 2018Co-Authors: Sriparna SahaAbstract:The current paper proposes a new genetic Clustering Technique using the concepts of line symmetry for assigning points to different clusters. The symmetrical line of a particular cluster is determined automatically using the search capability of genetic algorithms. This line is then used to compute the amount of symmetry of any point within a given cluster. The lines are encoded in the form of a chromosome. Mutation and crossover operations are modified in such a way so that those can help the GA to search for the symmetrical line efficiently. A way of measuring the amount of line symmetry of a given point with respect to a symmetrical line is also thoroughly described which is used further to assign points to different clusters. This in turn produces the partitioning corresponding to a particular chromosome. The compactness of this obtained partitioning is calculated using the line symmetry based measurement and is further used as the objective function of the chromosome. The proposed method is able to detect clusters having line symmetry property. The effectiveness of the proposed Technique (LSGA) is shown for 12 artificial and two real-life data sets. Results are compared with those obtained by existing genetic algorithm with line symmetry based Clustering Technique (GALS), genetic algorithm based K-means Clustering Technique (GAK-means), average linkage Clustering Technique, spectral Clustering Technique, expectation maximization based Clustering Technique, fuzzy-GA and point-GA Clustering Techniques.
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gene expression classification using a fuzzy point symmetry based pso Clustering Technique
Soft Computing, 2015Co-Authors: Sriparna SahaAbstract:The growth of biomedical and biological research has changed the shape after introduction of microarray technology. Several unsupervised Clustering Techniques have been introduced in order to explain and interpret the microarray gene expression data sets. A new Clustering Technique using fuzzy point symmetric concept has been proposed which utilizes particle swarm optimization as the underline optimization strategy. This paper has deployed the Clustering of microarray data as a single objective optimization problem. The efficacy of the proposed fuzzy Clustering Technique which poses the symmetric property is compared with some well known Clustering algorithms utilizing the properties of symmetry and genetic algorithms over some gene-microarray datasets which are publicly available. Biological and statistical analysis have been carried out to validate the obtained Clustering results.
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A new semi-supervised Clustering Technique using multi-objective optimization
Applied Intelligence, 2015Co-Authors: Abhay Kumar Alok, Sriparna Saha, Asif EkbalAbstract:Semi-supervised Clustering Techniques have been proposed in the literature to overcome the problems associated with unsupervised and supervised classification. It considers a small amount of labeled data and the whole data distribution during the process of Clustering a data. In this paper, a new approach towards semi-supervised Clustering is implemented using multiobjective optimization (MOO) framework. Four objective functions are optimized using the search capability of a multiobjective simulated annealing based Technique, AMOSA. These objective functions are based on some unsupervised and supervised information. First three objective functions represent, respectively, the goodness of the partitioning in terms of Euclidean distance, total symmetry present in the clusters and the cluster connectedness. For the last objective function, we have considered different external cluster validity indices, including adjusted rand index, rand index, a newly developed min-max distance based MMI index, NMMI index and Minkowski Score. Results show that the proposed semi-supervised Clustering Technique can effectively detect the appropriate number of clusters as well as the appropriate partitioning from the data sets having either well-separated clusters of any shape or symmetrical clusters with or without overlaps. Twenty four artificial and five real-life data sets have been used in the evaluation. We develop five different versions of Semi-GenClustMOO Clustering Technique by varying the external cluster validity indices. Obtained partitioning results are compared with another recently developed multiobjective semi-supervised Clustering Technique, Mock-Semi. At the end of the paper the effectiveness of the proposed Semi-GenClustMOO Clustering Technique is shown in segmenting one remote sensing satellite image on the part from the city of Kolkata.
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A new multiobjective Clustering Technique based on the concepts of stability and symmetry
Knowledge and Information Systems, 2010Co-Authors: Sriparna Saha, S. BandyopadhyayAbstract:Most Clustering algorithms operate by optimizing (either implicitly or explicitly) a single measure of cluster solution quality. Such methods may perform well on some data sets but lack robustness with respect to variations in cluster shape, proximity, evenness and so forth. In this paper, we have proposed a multiobjective Clustering Technique which optimizes simultaneously two objectives, one reflecting the total cluster symmetry and the other reflecting the stability of the obtained partitions over different bootstrap samples of the data set. The proposed algorithm uses a recently developed simulated annealing-based multiobjective optimization Technique, named AMOSA, as the underlying optimization strategy. Here, points are assigned to different clusters based on a newly defined point symmetry-based distance rather than the Euclidean distance. Results on several artificial and real-life data sets in comparison with another multiobjective Clustering Technique, MOCK, three single objective genetic algorithm-based automatic Clustering Techniques, VGAPS Clustering, GCUK Clustering and HNGA Clustering, and several hybrid methods of determining the appropriate number of clusters from data sets show that the proposed Technique is well suited to detect automatically the appropriate number of clusters as well as the appropriate partitioning from data sets having point symmetric clusters. The performance of AMOSA as the underlying optimization Technique in the proposed Clustering algorithm is also compared with PESA-II, another evolutionary multiobjective optimization Technique.
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A symmetry based multiobjective Clustering Technique for automatic evolution of clusters
Pattern Recognition, 2010Co-Authors: Sriparna Saha, S. BandyopadhyayAbstract:In this paper the problem of automatic Clustering a data set is posed as solving a multiobjective optimization (MOO) problem, optimizing a set of cluster validity indices simultaneously. The proposed multiobjective Clustering Technique utilizes a recently developed simulated annealing based multiobjective optimization method as the underlying optimization strategy. Here variable number of cluster centers is encoded in the string. The number of clusters present in different strings varies over a range. The points are assigned to different clusters based on the newly developed point symmetry based distance rather than the existing Euclidean distance. Two cluster validity indices, one based on the Euclidean distance, XB-index, and another recently developed point symmetry distance based cluster validity index, Sym-index, are optimized simultaneously in order to determine the appropriate number of clusters present in a data set. Thus the proposed Clustering Technique is able to detect both the proper number of clusters and the appropriate partitioning from data sets either having hyperspherical clusters or having point symmetric clusters. A new semi-supervised method is also proposed in the present paper to select a single solution from the final Pareto optimal front of the proposed multiobjective Clustering Technique. The efficacy of the proposed algorithm is shown for seven artificial data sets and six real-life data sets of varying complexities. Results are also compared with those obtained by another multiobjective Clustering Technique, MOCK, two single objective genetic algorithm based automatic Clustering Techniques, VGAPS Clustering and GCUK Clustering.
S. Bandyopadhyay - One of the best experts on this subject based on the ideXlab platform.
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A new multiobjective Clustering Technique based on the concepts of stability and symmetry
Knowledge and Information Systems, 2010Co-Authors: Sriparna Saha, S. BandyopadhyayAbstract:Most Clustering algorithms operate by optimizing (either implicitly or explicitly) a single measure of cluster solution quality. Such methods may perform well on some data sets but lack robustness with respect to variations in cluster shape, proximity, evenness and so forth. In this paper, we have proposed a multiobjective Clustering Technique which optimizes simultaneously two objectives, one reflecting the total cluster symmetry and the other reflecting the stability of the obtained partitions over different bootstrap samples of the data set. The proposed algorithm uses a recently developed simulated annealing-based multiobjective optimization Technique, named AMOSA, as the underlying optimization strategy. Here, points are assigned to different clusters based on a newly defined point symmetry-based distance rather than the Euclidean distance. Results on several artificial and real-life data sets in comparison with another multiobjective Clustering Technique, MOCK, three single objective genetic algorithm-based automatic Clustering Techniques, VGAPS Clustering, GCUK Clustering and HNGA Clustering, and several hybrid methods of determining the appropriate number of clusters from data sets show that the proposed Technique is well suited to detect automatically the appropriate number of clusters as well as the appropriate partitioning from data sets having point symmetric clusters. The performance of AMOSA as the underlying optimization Technique in the proposed Clustering algorithm is also compared with PESA-II, another evolutionary multiobjective optimization Technique.
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A symmetry based multiobjective Clustering Technique for automatic evolution of clusters
Pattern Recognition, 2010Co-Authors: Sriparna Saha, S. BandyopadhyayAbstract:In this paper the problem of automatic Clustering a data set is posed as solving a multiobjective optimization (MOO) problem, optimizing a set of cluster validity indices simultaneously. The proposed multiobjective Clustering Technique utilizes a recently developed simulated annealing based multiobjective optimization method as the underlying optimization strategy. Here variable number of cluster centers is encoded in the string. The number of clusters present in different strings varies over a range. The points are assigned to different clusters based on the newly developed point symmetry based distance rather than the existing Euclidean distance. Two cluster validity indices, one based on the Euclidean distance, XB-index, and another recently developed point symmetry distance based cluster validity index, Sym-index, are optimized simultaneously in order to determine the appropriate number of clusters present in a data set. Thus the proposed Clustering Technique is able to detect both the proper number of clusters and the appropriate partitioning from data sets either having hyperspherical clusters or having point symmetric clusters. A new semi-supervised method is also proposed in the present paper to select a single solution from the final Pareto optimal front of the proposed multiobjective Clustering Technique. The efficacy of the proposed algorithm is shown for seven artificial data sets and six real-life data sets of varying complexities. Results are also compared with those obtained by another multiobjective Clustering Technique, MOCK, two single objective genetic algorithm based automatic Clustering Techniques, VGAPS Clustering and GCUK Clustering.
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A new multiobjective simulated annealing based Clustering Technique using symmetry
Pattern Recognition Letters, 2009Co-Authors: Sriparna Saha, S. BandyopadhyayAbstract:In this paper, we have proposed a multiobjective Clustering Technique which optimizes simultaneously two objectives, one reflecting the total 'goodness' present in the data set in terms of total compactness (measured using Euclidean distance) of the clusters, and the other reflecting the total symmetry present in the clusters of the data set. The proposed algorithm uses a simulated annealing based multiobjective optimization method as the underlying optimization criterion. Center based encoding is used. The proposed multiobjective Clustering Technique is able to suitably evolve these cluster centers in such a way so that the two objectives are optimized 'simultaneously'. Assignment of points to different clusters is done based on the newly developed point symmetry based distance rather than the Euclidean distance. Results on eight artificial and six real-life data sets show that the proposed Technique is well-suited to detect true partitioning from data sets with clusters having either the hyperspherical shape or point symmetric structure. Results are compared with those obtained by five existing Clustering Techniques, one multiobjective Clustering Technique, MOCK, average linkage Clustering algorithm, expectation maximization Clustering algorithm, well-known genetic algorithm based K-means Clustering Technique (GAK-means) and a newly developed genetic algorithm with point symmetry based Clustering Technique (GAPS).
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a point symmetry based Clustering Technique for automatic evolution of clusters
IEEE Transactions on Knowledge and Data Engineering, 2008Co-Authors: S. Bandyopadhyay, Sriparna SahaAbstract:In this paper, a new symmetry-based genetic Clustering algorithm is proposed which automatically evolves the number of clusters as well as the proper partitioning from a data set. Strings comprise both real numbers and the don't care symbol in order to encode a variable number of clusters. Here, assignment of points to different clusters are done based on a point symmetry (PS)-based distance rather than the Euclidean distance. A newly proposed PS-based cluster validity index, sym-index, is used as a measure of the validity of the corresponding partitioning. The algorithm is, therefore, able to detect both convex and nonconvex clusters irrespective of their sizes and shapes as long as they possess the symmetry property. Kd-tree-based nearest neighbor search is used to reduce the complexity of computing PS-based distance. A proof on the convergence property of variable string length genetic algorithm with PS- distance-based Clustering (VGAPS-Clustering) Technique is also provided. The effectiveness of VGAPS-Clustering compared to variable string length genetic K-means algorithm (GCUK-Clustering) and one recently developed weighted sum validity function-based hybrid niching genetic algorithm (HNGA-Clustering) is demonstrated for nine artificial and five real-life data sets.
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mri brain image segmentation by fuzzy symmetry based genetic Clustering Technique
Congress on Evolutionary Computation, 2007Co-Authors: Sriparna Saha, S. BandyopadhyayAbstract:In this paper, an automatic segmentation Technique of multispectral magnetic resonance image of the brain using a new fuzzy point symmetry based genetic Clustering Technique is proposed. The proposed real-coded variable string length genetic fuzzy Clustering Technique (fuzzy-VGAPS) is able to evolve the number of clusters present in the data set automatically. Here, assignment of points to different clusters are made based on the point symmetry based distance rather than the Euclidean distance. The cluster centers are encoded in the chromosomes, whose value may vary. A newly developed fuzzy point symmetry based cluster validity index, FSym-index, is used as a measure of 'goodness' of the corresponding partition. This validity index is able to correctly indicate presence of clusters of different sizes as long as they are internally symmetrical. A Kd-tree based data structure is used to reduce the complexity of computing the symmetry distance. The proposed method is applied on several simulated T1-weighted, T2-weighted and proton density normal and MS lesion magnetic resonance brain images. Superiority of the proposed method over fuzzy C-means, expectation maximization, fuzzy variable string length genetic algorithm (fuzzy-VGA) Clustering algorithms are demonstrated quantitatively. The automatic segmentation obtained by fuzzy-VGAPS Clustering Technique is also compared with the available ground truth information.
Asif Ekbal - One of the best experts on this subject based on the ideXlab platform.
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A new semi-supervised Clustering Technique using multi-objective optimization
Applied Intelligence, 2015Co-Authors: Abhay Kumar Alok, Sriparna Saha, Asif EkbalAbstract:Semi-supervised Clustering Techniques have been proposed in the literature to overcome the problems associated with unsupervised and supervised classification. It considers a small amount of labeled data and the whole data distribution during the process of Clustering a data. In this paper, a new approach towards semi-supervised Clustering is implemented using multiobjective optimization (MOO) framework. Four objective functions are optimized using the search capability of a multiobjective simulated annealing based Technique, AMOSA. These objective functions are based on some unsupervised and supervised information. First three objective functions represent, respectively, the goodness of the partitioning in terms of Euclidean distance, total symmetry present in the clusters and the cluster connectedness. For the last objective function, we have considered different external cluster validity indices, including adjusted rand index, rand index, a newly developed min-max distance based MMI index, NMMI index and Minkowski Score. Results show that the proposed semi-supervised Clustering Technique can effectively detect the appropriate number of clusters as well as the appropriate partitioning from the data sets having either well-separated clusters of any shape or symmetrical clusters with or without overlaps. Twenty four artificial and five real-life data sets have been used in the evaluation. We develop five different versions of Semi-GenClustMOO Clustering Technique by varying the external cluster validity indices. Obtained partitioning results are compared with another recently developed multiobjective semi-supervised Clustering Technique, Mock-Semi. At the end of the paper the effectiveness of the proposed Semi-GenClustMOO Clustering Technique is shown in segmenting one remote sensing satellite image on the part from the city of Kolkata.
Abhay Kumar Alok - One of the best experts on this subject based on the ideXlab platform.
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A new semi-supervised Clustering Technique using multi-objective optimization
Applied Intelligence, 2015Co-Authors: Abhay Kumar Alok, Sriparna Saha, Asif EkbalAbstract:Semi-supervised Clustering Techniques have been proposed in the literature to overcome the problems associated with unsupervised and supervised classification. It considers a small amount of labeled data and the whole data distribution during the process of Clustering a data. In this paper, a new approach towards semi-supervised Clustering is implemented using multiobjective optimization (MOO) framework. Four objective functions are optimized using the search capability of a multiobjective simulated annealing based Technique, AMOSA. These objective functions are based on some unsupervised and supervised information. First three objective functions represent, respectively, the goodness of the partitioning in terms of Euclidean distance, total symmetry present in the clusters and the cluster connectedness. For the last objective function, we have considered different external cluster validity indices, including adjusted rand index, rand index, a newly developed min-max distance based MMI index, NMMI index and Minkowski Score. Results show that the proposed semi-supervised Clustering Technique can effectively detect the appropriate number of clusters as well as the appropriate partitioning from the data sets having either well-separated clusters of any shape or symmetrical clusters with or without overlaps. Twenty four artificial and five real-life data sets have been used in the evaluation. We develop five different versions of Semi-GenClustMOO Clustering Technique by varying the external cluster validity indices. Obtained partitioning results are compared with another recently developed multiobjective semi-supervised Clustering Technique, Mock-Semi. At the end of the paper the effectiveness of the proposed Semi-GenClustMOO Clustering Technique is shown in segmenting one remote sensing satellite image on the part from the city of Kolkata.
Antonio Giaquinto - One of the best experts on this subject based on the ideXlab platform.
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an unsupervised multi swarm Clustering Technique for image segmentation
Swarm and evolutionary computation, 2013Co-Authors: G Fornarelli, Antonio GiaquintoAbstract:Abstract Methods based on Particle Swarm Optimization represent efficient tools to solve a wide class of problems. In particular, they have been successfully applied to data Clustering and image processing. In this paper a multi-swarm Clustering Technique to perform an image segmentation is proposed. The search of the gray levels segmenting the image is carried out by a two-stage procedure. The former is performed by a traditional swarm population, moving in the search space according to a minimum distance criterion. The latter exploits a structure composed by identical swarms that refine the solution of the previous step. The combination of the two swarm approaches allows to tackle the drawbacks of the classical paradigm without making use of a complex implementation. The method is unsupervised, since it identifies the actual number of gray levels to segment the image automatically. Such characteristic is fundamental in the application of image segmentation to real cases, where generally the optimal number of centers is not known a priori and the algorithms are required to face possible environment variations. The conducted experiments show that the proposed Technique is able to yield adequate segmentations with a limited computational time, proving to be an interesting tool to face cases in which urgent time constraints have to be satisfied.