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ACM SIGMOD Record, 2002
Clustering is an unsupervised process since there are no predefined classes and no examples that would indicate grouping properties in the data set. The majority of the clustering algorithms behave differently depending on the features of the data set and the initial assumptions for defining groups.
Maria Halkidi +2 more
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Clustering is an unsupervised process since there are no predefined classes and no examples that would indicate grouping properties in the data set. The majority of the clustering algorithms behave differently depending on the features of the data set and the initial assumptions for defining groups.
Maria Halkidi +2 more
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Pattern Recognition, 1982
Abstract The quantitative evaluation of clusters has lagged far behind the development of clustering algorithms. This paper introduces a new procedure, based on probability profiles, for judging the validity of clusters established from rank-order proximity data. Probability profiles furnish a comprehensive picture of the compactness and isolation of
Thomas A. Bailey, Richard Dubes
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Abstract The quantitative evaluation of clusters has lagged far behind the development of clustering algorithms. This paper introduces a new procedure, based on probability profiles, for judging the validity of clusters established from rank-order proximity data. Probability profiles furnish a comprehensive picture of the compactness and isolation of
Thomas A. Bailey, Richard Dubes
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Cluster ensemble of valid small clusters
Journal of Intelligent & Fuzzy Systems, 2020During the last decade, ensemble clustering has been the subject of many researches in data mining. In ensemble clustering, several basic partitions are first generated and then a function is used for the clustering aggregation in order to create a final partition that is similar to all of the basic partitions as much as possible.
Li, Guang +4 more
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Proceedings of the International Conference on Learning and Optimization Algorithms: Theory and Applications, 2018
Cluster validity indexes are used to identify the best partitioning in a dataset from the results of a clustering algorithm. The overlap phenomenon is a source of failure for most of these validity indexes.In this work, we propose a new validity index named Vcw for the fuzzy c-means algorithm and we also propose to compare the performance of eight ...
Chaimae Ouchicha +2 more
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Cluster validity indexes are used to identify the best partitioning in a dataset from the results of a clustering algorithm. The overlap phenomenon is a source of failure for most of these validity indexes.In this work, we propose a new validity index named Vcw for the fuzzy c-means algorithm and we also propose to compare the performance of eight ...
Chaimae Ouchicha +2 more
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2011 IEEE International Conference on Systems, Man, and Cybernetics, 2011
A common question asked about unlabeled data sets is how many subsets (or clusters) of objects are represented in the data? The answer to this question is usually obtained by first clustering the data, and then employing a cluster validity measure to validate one or more candidate partitions of the objects.
Mihail Popescu +3 more
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A common question asked about unlabeled data sets is how many subsets (or clusters) of objects are represented in the data? The answer to this question is usually obtained by first clustering the data, and then employing a cluster validity measure to validate one or more candidate partitions of the objects.
Mihail Popescu +3 more
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Cluster validity for kernel fuzzy clustering
2012 IEEE International Conference on Fuzzy Systems, 2012This paper presents cluster validity for kernel fuzzy clustering. First, we describe existing cluster validity indices that can be directly applied to partitions obtained by kernel fuzzy clustering algorithms. Second, we show how validity indices that take dissimilarity (or relational) data D as input can be applied to kernel fuzzy clustering.
Timothy C. Havens +2 more
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Hierarchical clustering to validate fuzzy clustering
Proceedings of 1995 IEEE International Conference on Fuzzy Systems. The International Joint Conference of the Fourth IEEE International Conference on Fuzzy Systems and The Second International Fuzzy Engineering Symposium, 2002Fuzzy clustering is now extensively used for identification of (fuzzy) systems. Starting from a set of examples (input-output pairs) of a certain system, fuzzy clustering permits to disclose fuzzy rules governing the given system and also to make direct inference from new observations of the input.
M. Delgado, A. Gomez-Skarmeta, M.A. Vila
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Cluster Validity Measures for Fuzzy Two-Mode Clustering
2022Two-mode clustering consists in simultaneously partitioning rows (mode 1, e.g. objects) and columns (mode 2, e.g., variables) of a data matrix. Recently, several soft two-mode clustering techniques have been developed according to the fuzzy approach, but how to determine the optimal numbers of clusters for objects and variables is an open problem not ...
Maria Brigida Ferraro +2 more
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Document clustering based on cluster validation
Proceedings of the thirteenth ACM international conference on Information and knowledge management, 2004This paper presents a cluster validation based document clustering algorithm, which is capable of identifying both important feature words and true model order (cluster number). Important feature subset is selected by optimizing a cluster validity criterion subject to some constraint.
Zheng-Yu Niu, Dong-Hong Ji, Chew-Lim Tan
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Visual cluster validity for prototype generator clustering models
Pattern Recognition Letters, 2003Summary: Conventional cluster validity techniques usually represent all the validity information available about a particular clustering by a single number. The display method introduced here uses images generated from the results of any prototype generator clustering algorithm to do cluster validation.
Hathaway, Richard J., Bezdek, James C.
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