Journal ArticleDOI
A New Cluster Validity Index for Fuzzy Clustering
TLDR
A new “Graded Distance index” (GD_index) is proposed for computing optimal number of fuzzy clusters for a given data set and the efficiency of this index is compared with well-known existing indices and tested on several data sets.About:
This article is published in IFAC Proceedings Volumes.The article was published on 2013-12-01. It has received 21 citations till now. The article focuses on the topics: Fuzzy clustering & Cluster analysis.read more
Citations
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Proceedings ArticleDOI
Application of Fuzzy Clustering to Shaping Regional Development Strategies in Ukraine
TL;DR: The study presents the fuzzy classifications accompanied with the cluster validity process and substantial analysis of the economic indicators and allows to consider in more detail the similarities in the regional economic development levels, assigned to the same clusters, and reveal the dissimilarities between the regions assign to the different clusters.
Proceedings ArticleDOI
A New Data Mining Model for Forest-Fire Cellular Automata
Pingping Gu,Yuhui Zheng +1 more
TL;DR: A model of data mining for forest-fire CA based on Fuzzy KModes is designed and the results prove that some valuable essential rule about fire happening and forest growth is detected.
Proceedings ArticleDOI
Cluster validity index: Comparative study and a new validity index with high performance
TL;DR: A new validity index named Vcw is proposed for the fuzzy c-means algorithm and the performance of eight fairly recent cluster validity indexes are compared to select the best one between them that could give us the optimal number of clusters in the presence of a high overlap between the clusters.
Proceedings ArticleDOI
A new cluster validity index for type-2 fuzzy c-means algorithm
O. Mema Devi,Shahin Ara Begum +1 more
TL;DR: A new cluster validity index for the type-2 FCM called SM-index is proposed, based on the cluster fuzzy degree of FCM fuzzy set and the existing cluster validity indices that are suitable for FCM clustering algorithm.
Proceedings ArticleDOI
Comparison of Different Clustering Validity Methods in the Evaluation of Results for Electrical Fault Location in Industrial MV Network Using Fuzzy Clustering Technique
TL;DR: Comparison of three different Cluttering Validity methods shows that using Partition Entropy with Maximum Matrix normalization and 50% Alpha*cut gives the best effort saving in finding the fault.
References
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Journal ArticleDOI
Data clustering: a review
TL;DR: An overview of pattern clustering methods from a statistical pattern recognition perspective is presented, with a goal of providing useful advice and references to fundamental concepts accessible to the broad community of clustering practitioners.
Journal ArticleDOI
A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters
TL;DR: Two fuzzy versions of the k-means optimal, least squared error partitioning problem are formulated for finite subsets X of a general inner product space; in both cases, the extremizing solutions are shown to be fixed points of a certain operator T on the class of fuzzy, k-partitions of X, and simple iteration of T provides an algorithm which has the descent property relative to the least squarederror criterion function.
A fuzzy relative of the isodata process and its use in detecting compact well-separated clusters
TL;DR: In this paper, two fuzzy versions of the k-means optimal, least squared error partitioning problem are formulated for finite subsets X of a general inner product space, and the extremizing solutions are shown to be fixed points of a certain operator T on the class of fuzzy, k-partitions of X, and simple iteration of T provides an algorithm which has the descent property relative to the LSE criterion function.
Journal ArticleDOI
A validity measure for fuzzy clustering
X.L. Xie,Gerardo Beni +1 more
TL;DR: The authors present a fuzzy validity criterion based on a validity function which identifies compact and separate fuzzy c-partitions without assumptions as to the number of substructures inherent in the data.
Journal ArticleDOI
Cluster Validity with Fuzzy Sets
TL;DR: This paper uses membership function matrices associated with fuzzy c-partitions of X, together with their values in the Euclidean (matrix) norm, to formulate an a posteriori method for evaluating algorithmically suggested clusterings of X.