Topic
Davies–Bouldin index
About: Davies–Bouldin index is a research topic. Over the lifetime, 59 publications have been published within this topic receiving 13850 citations.
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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.
14,144 citations
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TL;DR: This article evaluates the performance of three clustering algorithms, hard K-Means, single linkage, and a simulated annealing (SA) based technique, in conjunction with four cluster validity indices, namely Davies-Bouldin index, Dunn's index, Calinski-Harabasz index, andA recently developed index I.
Abstract: In this article, we evaluate the performance of three clustering algorithms, hard K-Means, single linkage, and a simulated annealing (SA) based technique, in conjunction with four cluster validity indices, namely Davies-Bouldin index, Dunn's index, Calinski-Harabasz index, and a recently developed index I. Based on a relation between the index I and the Dunn's index, a lower bound of the value of the former is theoretically estimated in order to get unique hard K-partition when the data set has distinct substructures. The effectiveness of the different validity indices and clustering methods in automatically evolving the appropriate number of clusters is demonstrated experimentally for both artificial and real-life data sets with the number of clusters varying from two to ten. Once the appropriate number of clusters is determined, the SA-based clustering technique is used for proper partitioning of the data into the said number of clusters.
1,247 citations
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01 Jun 1998TL;DR: This work reviews two clustering algorithms and three indexes of crisp cluster validity and shows that while Dunn's original index has operational flaws, the concept it embodies provides a rich paradigm for validation of partitions that have cloud-like clusters.
Abstract: We review two clustering algorithms (hard c-means and single linkage) and three indexes of crisp cluster validity (Hubert's statistics, the Davies-Bouldin index, and Dunn's index). We illustrate two deficiencies of Dunn's index which make it overly sensitive to noisy clusters and propose several generalizations of it that are not as brittle to outliers in the clusters. Our numerical examples show that the standard measure of interset distance (the minimum distance between points in a pair of sets) is the worst (least reliable) measure upon which to base cluster validation indexes when the clusters are expected to form volumetric clouds. Experimental results also suggest that intercluster separation plays a more important role in cluster validation than cluster diameter. Our simulations show that while Dunn's original index has operational flaws, the concept it embodies provides a rich paradigm for validation of partitions that have cloud-like clusters. Five of our generalized Dunn's indexes provide the best validation results for the simulations presented.
1,108 citations
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TL;DR: The searching capability of genetic algorithms has been exploited for automatically evolving the number of clusters as well as proper clustering of any data set and the proposed technique is able to distinguish some characteristic landcover types in the image.
417 citations
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01 Dec 2006TL;DR: Four channel of myoelectric signal from upper limb muscles are used in this paper to classify six distinctive activities and prove more accurate and reliable classification for the elite subset of features applying to artificial neural networks as the classifier.
Abstract: This paper presents an ongoing investigation to select optimal subset of features from set of well-known myoelectric signals (MES) features in time and frequency domains. Four channel of myoelectric signal from upper limb muscles are used in this paper to classify six distinctive activities. Cascaded genetic algorithm (GA) has been adopted as the search strategy in feature subset selection. Davies-Bouldin index (DBI) and Fishers linear discriminant index (FLDI) are employed as the filter objective functions and linear discriminant analysis (LDA) has been used as the wrapper objective function. Results prove more accurate and reliable classification for the elite subset of features applying to artificial neural networks as the classifier.
88 citations