A fuzzy modeling approach to cluster validity

Capitaine, Hoël Le; Frélicot, Carl

doi:10.1109/fuzzy.2009.5277127

Cited by 3 publications

(5 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Almost all mentioned methods only based on distortion information from within clusters. Hoel Le Capitaine and Carl Frélicot [5] introduce an approach to find the optimal number of clusters of a fuzzy partition. They use measures of separation and degree of overlap of the clusters based on triangular norms and a discrete Sugeno integral.…”

Section: Related Workmentioning

confidence: 99%

“…f) Based on the matrix U, x i is arranged into clusters according to rules as follows: x i will belong to any cluster that it has the greatest degree g) Get all extremely marginal objects of all clusters h) Compute γ based on (1), (2), (3), (4), (5) as in section B, part III. 5) Select c best when γ get a local minimum value after γ fluctuates fast and starts to have stable trend.…”

Section: ) Input N Objects X I Fuzzy Parameter M > 1 Epsilon Smalmentioning

confidence: 99%

“…In this section, we compare our results with results of authors in [5] and number of clusters in fact on some data sets. Results are presented in Table V that shows efficiency of our method.…”

Section: Comparisionmentioning

confidence: 99%

“…The FCM++'s experimental results on some data sets of UCI is matched with number of clusters of data sets in fact. We compare our results with results of authors in [5] and show encouraging results.…”

Section: Comparisionmentioning

confidence: 99%

“…Section V presents experimental results on some data sets of UCI: Iris, Wine, Soybean-small. Section VI compares our results with results of authors in [5]. Section VII is conclusion of the paper.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

An adaptive method to determine the number of clusters in clustering process

Doan

Nguyen

2014

2014 International Conference on Computer and Information Sciences (ICCOINS)

View full text Add to dashboard Cite

A difficult problem of most clustering algorithms is how to specify the appropriate number of clusters. This paper proposes an adaptive method of selecting of number of clusters in clustering process by making coefficients indicated the appropriate number of clusters. The intra-cluster coefficient reflects intra distortion of cluster through maximum distance and a mean distance of cluster's extremely marginal objects. The inter-cluster coefficient reflects distance among clusters. It is ratio between closest distance from this cluster's centre to an extremely marginal object of other cluster and mean distance from this cluster's centre to all of extremely marginal object of other cluster respectively. A new coefficient that indicates the appropriate number of clusters is build from the intra-cluster coefficient and inter-cluster coefficient. The looking for extremely marginal objects and the new coefficient are integrated in a weighted FCM algorithm and it is calculated adaptively while the weighted FCM is processing. The weighted FCM algorithm integrated new coefficient is called FCM++. We experiment with FCM++ on some data sets of UCI: Iris, Wine, Soybean-small and show encouraging results. Keywords-clustering algorithm; determine the number of clusters; fuzzy c-means.I.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: ) Input N Objects X I Fuzzy Parameter M > 1 Epsilon Smalmentioning

confidence: 99%

Section: Comparisionmentioning

confidence: 99%

Section: Comparisionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

An adaptive method to determine the number of clusters in clustering process

Doan

Nguyen

2014

2014 International Conference on Computer and Information Sciences (ICCOINS)

View full text Add to dashboard Cite

show abstract

Optimal K-Means Clustering Algorithm for Weblog Mining

Jain¹,

Kashyap²

2021

2021 10th IEEE International Conference on Communication Systems and Network Technologies (CSNT)

View full text Add to dashboard Cite

A Cluster-Validity Index Combining an Overlap Measure and a Separation Measure Based on Fuzzy-Aggregation Operators

Capitaine

Frélicot

2011

IEEE Trans. Fuzzy Syst.

Self Cite

View full text Add to dashboard Cite

Abstract-Since a clustering algorithm can produce as many partitions as desired, one needs to assess their quality in order to select the partition that most represents the structure in the data if there is any. This is the rationale for the cluster validity problem and indices. This paper presents a cluster validity index that helps to find the optimal number of clusters of data from partitions generated by a fuzzy clustering algorithm such as the Fuzzy C-Means (FCM) or its derivatives. Given a fuzzy partition, this new index uses a measure of multiple clusters overlap and a separation measure for each data point, both based on an aggregation operation of membership degrees. Experimental results on artificial and benchmark data sets are given to demonstrate the performance of the proposed index as compared to traditional and recent indices.

show abstract

A fuzzy modeling approach to cluster validity

Cited by 3 publications

References 18 publications

An adaptive method to determine the number of clusters in clustering process

An adaptive method to determine the number of clusters in clustering process

Optimal K-Means Clustering Algorithm for Weblog Mining

A Cluster-Validity Index Combining an Overlap Measure and a Separation Measure Based on Fuzzy-Aggregation Operators

Contact Info

Product

Resources

About