Improvement of Initial Cluster Center of C-means using Teaching Learning based Optimization

Naik, Anima; Satapathy, Suresh Chandra; Parvathi, K.

doi:10.1016/j.protcy.2012.10.051

Cited by 33 publications

(12 citation statements)

References 14 publications

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“…Step (4) assigns the data to their nearest two clusters till a given threshold is reached. Once the data are assigned to their respective clusters, they are removed from the data set.…”

Section: B Methods -Ii: Far Efficient K-means (Fekm)mentioning

confidence: 99%

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Efficient Intelligent Framework for Selection of Initial Cluster Centers

Mishra¹,

Rath²,

Nanda³

et al. 2019

IJISA

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At present majority of research is on cluster analysis which is based on information retrieval from data that portrays the objects and their association among them. When there is a talk on good cluster formation, then selection of an optimal cluster core or center is the necessary criteria. This is because an inefficient center may result in unpredicted outcomes. Hence, a sincere attempt had been made to offer few suggestions for discovering the near optimal cluster centers. We have looked at few versatile approaches of data clustering like K-Means, TLBOC, FEKM, FECA and MCKM which differs in their initial center selection procedure. They have been implemented on diverse data sets and their inter and intra cluster formation efficiency were tested using different validity indices. The clustering accuracy was also conducted using Rand index criteria. All the algorithms computational complexity was analyzed and finally their computation time was also recorded. As expected, mostly FECA and to some extend FEKM and MCKM confers better clustering results as compared to K-Means and TLBOC as the former ones manages to obtain near optimal cluster centers. More specifically, the accuracy percentage of FECA is higher than the other techniques however, it's computational complexity and running time is moderately higher.

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“…Step (4) assigns the data to their nearest two clusters till a given threshold is reached. Once the data are assigned to their respective clusters, they are removed from the data set.…”

Section: B Methods -Ii: Far Efficient K-means (Fekm)mentioning

confidence: 99%

“…When clustering was achieved by means of fuzzy c-means approach, the determination of initial centre plays an important role in its final consequence. TLBO as suggested by [4] presents a solution to this issue. TLBO was initially used to determine the near-optimal cluster centers.…”

Section: Related Workmentioning

confidence: 99%

Efficient Intelligent Framework for Selection of Initial Cluster Centers

Mishra¹,

Rath²,

Nanda³

et al. 2019

IJISA

View full text Add to dashboard Cite

show abstract

“…To overcome these drawbacks, many clustering algorithms have been introduced recently. Some 14,15 focus on the objective function optimization and others 16,17 apply other clustering method to determine initial centers instead of random centers. Also using various kernels in FCM leads to different clustering.…”

Section: Fuzzy C-meansmentioning

confidence: 99%

A Definition for Hesitant fuzzy Partitions

Aliahmadipour¹,

Torra²,

Eslami³

et al. 2016

IJCIS

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In this paper, we define hesitant fuzzy partitions (H-fuzzy partitions) to consider the results of standard fuzzy clustering family (e.g. fuzzy c-means and intuitionistic fuzzy c-means). We define a method to construct H-fuzzy partitions from a set of fuzzy clusters obtained from several executions of fuzzy clustering algorithms with various initialization of their parameters. Our purpose is to consider some local optimal solutions to find a global optimal solution also letting the user to consider various reliable membership values and cluster centers to evaluate her/his problem using different cluster validity indices.

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“…Anima Naik [8] advanced the TLBO (Teaching Learning Based Optimization) to address the problem of initializing centers of clusters, in which the search space of given dataset was used to find out near-optimal cluster centers and taken use of reformulated c-mean objective function to evaluate centers.…”

Section: Review Of Initializing Clustering For Numerical Datamentioning

confidence: 99%

Initialization of k-modes clustering for categorical data

Chen

Jin

et al. 2013

2013 International Conference on Management Science and Engineering 20th Annual Conference Proceedings

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The k-modes clustering algorithm is undoubtedly one of the most widely used partitional algorithms for categorical data. Unfortunately, due to its gradient descent nature, this algorithm is highly sensitive to the initialization of clustering. Categorical initialization methods have been proposed to address this problem. In this paper, we present an overview of initialization methods of clustering for numerical data and categorical data respectively with an emphasis on their computational efficiency. We then propose a new initialization method for categorical data, which can obtain the good initial cluster centers using the new distance base on the RD, and explore the methods of density and grid. Finally, proposed method has been tested on diagnosis dataset, a real world data set from UCI Machine Learning Repository, and been analyzed the experimental results, which illustrates that the proposed method is effective and efficient for initializing categorical data.

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Improvement of Initial Cluster Center of C-means using Teaching Learning based Optimization

Cited by 33 publications

References 14 publications

Efficient Intelligent Framework for Selection of Initial Cluster Centers

Efficient Intelligent Framework for Selection of Initial Cluster Centers

A Definition for Hesitant fuzzy Partitions

Initialization of k-modes clustering for categorical data

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