Results 261 to 270 of about 346,399 (298)

Enhanced fuzzy clustering algorithm and cluster validity index for human perception

Expert Systems with Applications, 2013
In this study, we propose an enhanced fuzzy clustering algorithm related to a-cut interval descriptions of fuzzy numbers and a new cluster validity index, which occurs by a-cut intervals and adding two ad-hoc functions in the compactness and separability measures.
Türkşen, İsmail Burhan, Başkır, M. Bahar   +1 more
openaire   +2 more sources

Novel Cluster Validity Index for FCM Algorithm

Journal of Computer Science and Technology, 2006
How to determine an appropriate number of clusters is very important when implementing a specific clustering algorithm, like c-means, fuzzy c-means (FCM). In the literature, most cluster validity indices are originated from partition or geometrical property of the data set.
Jian Yu, Cui-Xia Li
openaire   +1 more source

An Automatic Index Validity for Clustering

2010
Many validity index algorithms have been proposed to determine the number of clusters. These methods usually employ the Euclidean distance as the measurement. However, it is difficult for the Euclidean distance metric to evaluate the compactness of data when non-linear relationship exists between different components of data.
Zizhu Fan, Xiangang Jiang, Baogen Xu, Zhaofeng Jiang   +3 more
openaire   +1 more source

A new improved cluster validity indexing technique: harnessed from Goodman-Kruskal validity index

International Journal of Information and Communication Technology, 2015
The true potential of clustering techniques is not harnessed optimally because of several reasons. Clustering is implemented either on the preclassified datasets or if implemented on unclassified datasets, it remains unacceptable because its validity cannot be established. Cluster validity techniques come to rescue in the latter cases. Several internal
Smita Prava Mishra, Debahuti Mishra, Srikanta Patnaik   +2 more
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A Cluster Validity Index for Hard Clustering

2012
This paper describes a new cluster validity index for the well-separable clusters in data sets. The validity indices are necessary for many clustering algorithms to assign the naturally existing clusters correctly. In the presented method, to determine the optimal number of clusters in data sets, the new cluster validity index has been used.
openaire   +1 more source

A Bounded Index for Cluster Validity

2007
Clustering is one of the most well known types of unsupervised learning. Evaluating the quality of results and determining the number of clusters in data is an important issue. Most current validity indices only cover a subset of important aspects of clusters. Moreover, these indices are relevant only for data sets containing at least two clusters.
Sandro Saitta, Benny Raphael, Ian F. C. Smith   +2 more
openaire   +1 more source

A new validity index for fuzzy clustering

10th IEEE International Conference on Fuzzy Systems. (Cat. No.01CH37297), 2002
This paper presents a new validity index for fuzzy clustering called a partition separation (PS) index. It uses the factors of a normalized partition coefficient and an exponential separation measure. According to the numerical comparisons with the other five cluster-validity indexes, the proposed PS index shows its high ability to produce a valid ...
null Miin-Shen Yang, null Kuo-Lung Wu
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Validity index for crisp and fuzzy clusters

Pattern Recognition, 2004
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Pakhira, Malay K., Bandyopadhyay, Sanghamitra, Maulik, Ujjwal   +2 more
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A Density Discriminant Index for Cluster Validation

2019 11th International Conference on Information Technology and Electrical Engineering (ICITEE), 2019
Clustering analysis is widely applied in several domains of study. Using a suitable number of clusters is one of the most important factors to influence the performance of clustering. Several algorithms of cluster validation have been developed to find such a number.
Supphawarich Thanarattananakin, Praisan Padungweang, Worarat Krathu   +2 more
openaire   +1 more source

Validity index for clustering with penalizing method

2010 3rd International Symposium on Systems and Control in Aeronautics and Astronautics, 2010
One of the most difficult problems facing the user of clustering analysis techniques in practice is the objective assessment of the stability and validity of the clusters found by the numerical technique used. The problem of determining the “true” number of clusters has been called the fundamental problem of cluster validity.
null Jun Wang, null Xi-yuan Peng, null Yu Peng   +2 more
openaire   +1 more source