A Point Symmetry-Based Clustering Technique for Automatic Evolution of Clusters

Bandyopadhyay, Sanghamitra; Saha, Sriparna

doi:10.1109/tkde.2008.79

Cited by 139 publications

(104 citation statements)

References 31 publications

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“…In Bandyopadhyay and Saha, [43] the Variable String Length Point Symmetry-Based (VGAPS) clustering technique was presented. This technique is able to encoding of a variable number of clusters by means a variable string length GA.…”

Section: Moco: a Reviewmentioning

confidence: 99%

Bio-inspired multiobjective clustering optimization: A survey and a proposal

Cunha

Cruz

Politi

et al. 2017

AIR

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Multiobjective clustering techniques have been used to simultaneously consider several complementary aspects of clustering quality. They optimize two or more cluster validity indices simultaneously, they lead to high-quality results, and have emerged as attractive and robust alternatives for solving clustering problems. This paper provides a brief review of bio-Inspired multiobjective clustering, and proposes a bee-inspired multiobjective optimization (MOO) algorithm, named cOptBees-MO, to solve multiobjective data clustering problems. In its survey part, a brief tutorial on MOO and multiobjective clustering optimization (MOCO) is presented, followed by a review of the main works in the area. Particular attention is given to the many objective functions used in MOCO. To evaluate the performance of the algorithm it was executed for various datasets and the results presented high quality clusters, diverse solutions an the automatic determination of a suitable number of clusters.

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Section: Moco: a Reviewmentioning

confidence: 99%

Bio-inspired multiobjective clustering optimization: A survey and a proposal

Cunha

Cruz

Politi

et al. 2017

AIR

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show abstract

“…The first objective function is the point symmetry distance based cluster validity index, Sym-index (Bandyopadhyay and Saha 2008). This validity index quantifies the goodness of a partitioning in terms of symmetricity.…”

Section: Objective Functions Usedmentioning

confidence: 99%

Feature Selection and Semi-supervised Clustering Using Multiobjective Optimization

Alok

Saha

Ekbal

2014

2014 International Conference on Soft Computing and Machine Intelligence

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In this paper we have coupled feature selection problem with semi-supervised clustering. Semi-supervised clustering utilizes the information of unsupervised and supervised learning in order to overcome the problems related to them. But in general all the features present in the data set may not be important for clustering purpose. Thus appropriate selection of features from the set of all features is very much relevant from clustering point of view. In this paper we have solved the problem of automatic feature selection and semi-supervised clustering using multiobjective optimization. A recently created simulated annealing based multiobjective optimization technique titled archived multiobjective simulated annealing (AMOSA) is used as the underlying optimization technique. Here features and cluster centers are encoded in the form of a string. We assume that for each data set for 10% data points class level information are known to us. Two internal cluster validity indices reflecting different data properties, an external cluster validity index measuring the similarity between the obtained partitioning and the true labelling for 10% data points and a measure counting the number of features present in a particular string are optimized using the search capability of AMOSA. AMOSA is utilized to detect the appropriate subset of features, appropriate number of clusters as well as the appropriate partitioning from any given data set. The effectiveness of the proposed semi-supervised feature selection technique as compared to the existing techniques is shown for seven real-life data sets of varying complexities.

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“…Many of the data points are clustered incorrectly by using Euclidean distance while they are clustered correctly by using the new point symmetry-based distance measure. We used also the concept of [15] for calculating θ in our experiments, but as shown in Figure 6c, it is not effective with this data set and the clusters are clustered incorrectly. We tried manually to choose different values of θ to enhance the results of data clustering by using the new point symmetry-based distance measure.…”

Section: Performance Of Improved Ps-based Distance Measurementioning

confidence: 99%

K-means algorithm with a novel distance measure

Abudalfa¹,

Mikki²

2013

Turk J Elec Eng & Comp Sci

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Abstract:In this paper, we describe an essential problem in data clustering and present some solutions for it. We investigated using distance measures other than Euclidean type for improving the performance of clustering. We also developed an improved point symmetry-based distance measure and proved its efficiency. We developed a k-means algorithm with a novel distance measure that improves the performance of the classical k-means algorithm. The proposed algorithm does not have the worst-case bound on running time that exists in many similar algorithms in the literature.Experimental results shown in this paper demonstrate the effectiveness of the proposed algorithm. We compared the proposed algorithm with the classical k-means algorithm. We presented the proposed algorithm and their performance results in detail along with avenues of future research.

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A Point Symmetry-Based Clustering Technique for Automatic Evolution of Clusters

Cited by 139 publications

References 31 publications

Bio-inspired multiobjective clustering optimization: A survey and a proposal

Bio-inspired multiobjective clustering optimization: A survey and a proposal

Feature Selection and Semi-supervised Clustering Using Multiobjective Optimization

K-means algorithm with a novel distance measure

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