Results 251 to 260 of about 346,399 (298)

A New Cluster Validity Index for Fuzzy Clustering

IFAC Proceedings Volumes, 2013
Abstract Performance of any clustering algorithm depends critically on the number of clusters that are initialized. A practitioner might not know, a priori , the number of partitions into which his data should be divided; to address this issue many cluster validity indices have been proposed for finding the optimal number of partitions.
Sreeram Joopudi, Suraj S Rathi, S. Narasimhan, Raghunathan Rengaswamy   +3 more
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A cluster validity index for fuzzy clustering

Fuzzy Sets and Systems, 2010
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New Cluster Validity Index with Fuzzy Functions

2007
A new cluster validity index is introduced to validate the results obtained by the recent Improved Fuzzy Clustering (IFC), which combines two different methods, i.e., fuzzy c-means clustering and fuzzy c-regression, in a novel way. Proposed validity measure determines the optimum number of clusters of the IFC based on a ratio of the compactness to ...
Çelikyılmaz, Aslı, Türkşen, İsmail Burhan   +1 more
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Improved validation index for fuzzy clustering

Proceedings of the 2005, American Control Conference, 2005., 2005
This paper proposes a new validation index for fuzzy clustering in order to eliminate the monotonically decreasing tendency as the number of clusters approaches to the number of data points and avoid the numerical instability of validation index when fuzzy weighting exponent increases. Limit analyses of Xie-Beni index, Kwon index and the proposed index
null Yuangang Tang, null Fuchun Sun, null Zengqi Sun   +2 more
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MSTI: A New Clustering Validity Index for Hierarchical Clustering

2018 4th Annual International Conference on Network and Information Systems for Computers (ICNISC), 2018
The clustering validity index (CVI) is an important tool to measure the clustering effect and determine the optimal clustering number (K_opt). However, most of the existing CVIs cannot properly deal with some non-spherical distributions data sets and data sets with great differences in sample size and density among clusters.
Peng Li, Feng Liu, Er-Zhou Zhua
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Fuzzy cluster validation index based on inter-cluster proximity

Pattern Recognition Letters, 2003
A new cluster validity index is proposed for fuzzy partitions obtained from Fuzzy C-Means algorithm. The proposed validity index exploits an inter-cluster proximity between fuzzy clusters. The inter-cluster proximity is used to measure the degree of overlap between clusters. A low proximity value indicates well-partitioned clusters.
Kim, DW, Lee, KH Lee, Kwang-Hyung, Lee, D Lee, Doheon   +2 more
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A new index for clustering validation with overlapped clusters

Expert Systems with Applications, 2016
An index to compare clustering solutions with overlapped groups is proposed.The index is carefully designed with an intuitive probabilistic approach.Results with standard datasets for benchmarking are included.It has been applied also to a real application involving social networks.The index can measure correctly the similarity between clustering ...
D.N. Campo, G. Stegmayer, D.H. Milone
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Measuring Hybrid SC-FCM Clustering with Cluster Validity Index

2018 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI), 2018
Clustering classifies data into groups based on the similarity of each element of data. In order to validate the cluster, cluster validity index is introduced. Hybrid SC-FCM (Subtractive Clustering-Fuzzy C-Means) clustering method is a clustering technique to overcome the weakness of the FCM (Fuzzy C-Means) clustering.
Victor Utomo, Dhendra Marutho
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A cluster validity index for FCM-type co-clustering

2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2013
Cluster validation is an important issue in FCM-type clustering applications and many validity indices have been proposed for selecting the optimal fuzzy partition. In most of FCM-type cluster validity indices, the number of clusters is evaluated by considering the partition quality and geometrical features.
Mai Muranishi, Katsuhiro Honda, Akira Notsu, Hidetomo Ichihashi   +3 more
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A cluster validity index for fuzzy c-means clustering

2011 International Conference on System science, Engineering design and Manufacturing informatization, 2011
This paper presents a new validity index for validation of the fuzzy partitions generated by the fuzzy c-means algorithm. The proposed validity index is based on the compactness and separation measure. The compactness measure is defined as the weighted square deviation of the intra cluster, and the separation measure is defined as the distance for the ...
null Yating Hu, null Chuncheng Zuo, null Yang Yang, null Fuheng Qu   +3 more
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