A symmetry based multiobjective clustering technique for automatic evolution of clusters

Saha, Sriparna; Bandyopadhyay, Sanghamitra

doi:10.1016/j.patcog.2009.07.004

Cited by 112 publications

(51 citation statements)

References 32 publications

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“…Image segmentation is a multiple objective problem (Saha and Bandyopadhyay, 2010). It involves several processes such as pattern representation, feature selection, feature extraction and pattern proximity.…”

Section: Related Workmentioning

confidence: 99%

Object Recognition Based on Image Segmentation and Clustering

Thilagamani¹

2011

Journal of Computer Science

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Problem statement: This study deals with object recognition based on image segmentation and clustering. Acquiring prior information of an image is done via two separate processes. Approach: The first process deals with detecting object parts of an image and integration of detected parts into several clusters. All these cluster centers form the visual words. The second process deals with over segmenting the image into super pixels and formation of larger sub region using Mid-level clustering algorithm, since it incorporates various information to decide the homogeneity of a sub region. Results: The outcome of the two processes are used for the similarity graph representation for object segmentation as proposed. In order to model the relationship between the shape and color or texture matrix representation has been used. Mask map ensures that the probability of each super pixel to harp inside an object. Conclusion: The basic whim is to integrate all the priors into an uniform framework. Thus the ORBISC can handle size, color, texture and pose variations better than those methods that focus on the objects only.

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Section: Related Workmentioning

confidence: 99%

Object Recognition Based on Image Segmentation and Clustering

Thilagamani¹

2011

Journal of Computer Science

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“…In AMOSA based clustering, the states are made up of real numbers which represent the coordinates of the centers of the partitions similar to [6,11,12]. AMOSA attempts to evolve an appropriate set of cluster centers that represent the associated partitioning of the data.…”

Section: State Representation and Archive Initializationmentioning

confidence: 99%

“…In [10], a multiobjective clustering technique, named MOCK, is developed where two evaluation criteria, one based on the total compactness of the partitioning and another based on the connectedness of the clusters are optimized simultaneously. Some multiobjective clustering techniques were developed in [11,12,13]. …”

Section: Introductionmentioning

confidence: 99%

Use of symmetry and stability for data clustering

Saha

Maulik

2010

Evol. Intel.

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An important consideration in clustering is the determination of an algorithm appropriate for partitioning a given data set. Thereafter identification of the correct model order and determining the corresponding partitioning need to be performed. In this paper, at first the effectiveness of the recently developed symmetry based cluster validity index named Sym-index which provides a measure of ''symmetricity'' of the different partitionings of a data set is shown to address all the above mentioned issues, viz., identifying the appropriate clustering algorithm, determining the proper model order and evolving the proper partitioning as long as the clusters possess the property of symmetry. Results demonstrating the superiority of the proposed cluster validity measure in appropriately determining the proper clustering technique as well as appropriate model order as compared to five other recently proposed measures, namely PS-index, I-index, CS-index, well-known XB-index, and stability based index, are provided for several clustering methods viz., two recently developed genetic algorithm based clustering techniques, the average linkage clustering algorithm, self organizing map and the expectation maximization clustering algorithm. Five artificial data sets and three real life data sets, are considered for this purpose. In the second part of the paper, a new measure of stability of clustering solutions over different bootstrap samples of a data set is proposed. Thereafter a multiobjective optimization based clustering technique is developed which optimizes both Sym-index and the measure of stability simultaneously to automatically determine the appropriate number of clusters and the appropriate partitioning of the data sets having symmetrical shaped clusters. Results on five artificial and five real-life data sets show that the proposed technique is well-suited to detect the number of clusters from data sets having point symmetric clusters.

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“…In [20], a multiobjective clustering technique, MOCK, is proposed to recognize the appropriate partitioning from the data sets that contain either hyperspherical shaped clusters or well-separated clusters. In [21], a multiobjective clustering technique is proposed, called VAMOSA. The algorithm optimizes two clustering validity indices simultaneously, so that the algorithm can evolve proper partitioning from the clustering data set with any shape, size, or convexity.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Multiobjective Genetic Algorithm with Fuzzy c-Means for Automatic Data Clustering

Dong

Hao

Liu

2018

Mathematical Problems in Engineering

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This paper presents a fuzzy clustering method based on multiobjective genetic algorithm. The ADNSGA2-FCM algorithm was developed to solve the clustering problem by combining the fuzzy clustering algorithm (FCM) with the multiobjective genetic algorithm (NSGA-II) and introducing an adaptive mechanism. The algorithm does not need to give the number of clusters in advance. After the number of initial clusters and the center coordinates are given randomly, the optimal solution set is found by the multiobjective evolutionary algorithm. After determining the optimal number of clusters by majority vote method, the Jm value is continuously optimized through the combination of Canonical Genetic Algorithm and FCM, and finally the best clustering result is obtained. By using standard UCI dataset verification and comparing with existing single-objective and multiobjective clustering algorithms, the effectiveness of this method is proved.

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A symmetry based multiobjective clustering technique for automatic evolution of clusters

Cited by 112 publications

References 32 publications

Object Recognition Based on Image Segmentation and Clustering

Object Recognition Based on Image Segmentation and Clustering

Use of symmetry and stability for data clustering

An Adaptive Multiobjective Genetic Algorithm with Fuzzy c-Means for Automatic Data Clustering

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