Journal ArticleDOI
A new validity index for crisp clusters
TLDR
A new cluster validity index, called the STR index, is defined as the product of two components which determine changes of compactness and separability of clusters during a clustering process, and the maximum value identifies the best clustering scheme.Abstract:
In this paper, a new cluster validity index which can be considered as a measure of the accuracy of the partitioning of data sets is proposed. The new index, called the STR index, is defined as the product of two components which determine changes of compactness and separability of clusters during a clustering process. The maximum value of this index identifies the best clustering scheme. Three popular algorithms have been applied as underlying clustering techniques, namely complete-linkage, expectation maximization and K-means algorithms. The performance of the new index is demonstrated for several artificial and real-life data sets. Moreover, this new index has been compared with other well-known indices, i.e., Dunn, Davies-Bouldin, PBM and Silhouette indices, taking into account the number of clusters in a data set as the comparison criterion. The results prove superiority of the new index as compared to the above-mentioned indices.read more
Citations
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Journal ArticleDOI
Fast and stable clustering analysis based on Grid-mapping K-means algorithm and new clustering validity index
TL;DR: By mapping datasets to grids, a new clustering validity index (BCVI) is designed to better evaluate the quality of clustering results generated by the Grid-K-means algorithm, which is faster and more accurate than the traditional ones.
Journal ArticleDOI
Automatic Scientific Document Clustering Using Self-organized Multi-objective Differential Evolution
TL;DR: The effectiveness of the proposed approach, namely self-organizing map based multi-objective document clustering technique (SMODoc_clust) is shown in automatic classification of some scientific articles and web-documents.
Journal ArticleDOI
Cluster validation using an ensemble of supervised classifiers
Jorge Rodríguez,Miguel Angel Medina-Pérez,Andres Eduardo Gutierrez-Rodríguez,Raúl Monroy,Hugo Terashima-Marín +4 more
TL;DR: A new cluster validity index is proposed, which attempts to avoid this bias using an ensemble of distinct supervised classifiers, this way the bias is not attributable to a specific classifier, but to a collection thereof, hence alleviating the problem.
Journal ArticleDOI
A new cluster validity index using maximum cluster spread based compactness measure
M. Arif Wani,Romana Riyaz +1 more
TL;DR: A new compactness measure is introduced that depicts the typical behaviour of a cluster where more points are located around the centre and lesser points towards the outer edge of the cluster and a novel penalty function is proposed for determining the distinctness measure of clusters.
Journal ArticleDOI
A New Method for Automatic Determining of the DBSCAN Parameters
TL;DR: A new method that determines the right values of the parameters for different kinds of clusters is proposed that uses detection of sharp distance increases generated by a function which computes a distance between each element of a dataset and its k-th nearest neighbor.
References
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Journal ArticleDOI
Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
TL;DR: A new graphical display is proposed for partitioning techniques, where each cluster is represented by a so-called silhouette, which is based on the comparison of its tightness and separation, and provides an evaluation of clustering validity.