Topic
Dunn index
About: Dunn index is a research topic. Over the lifetime, 150 publications have been published within this topic receiving 24021 citations.
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01 Nov 2015
TL;DR: The algorithm has been implemented with the Iris data set and its validity and effectiveness is tested with the help of commonly used internal evaluation measures for clustering like Davies Boudlin Index and Dunn Index.
Abstract: The preeminent intention of the proposed study is exploring the performance of the Brainstorm Optimization algorithm in Hard c-means clustering of data. The rationale behind this analysis is to generate a random solution set of centroids and then modify the centroids so as to refine the clusters. As we are using Brainstorm Optimization which is a form of evolutionary algorithm this refinement of centroid happens through competition and cooperation with existing centroid values. This algorithm incorporates both exploitation and exploration of the search space to generate the new centroids. The algorithm has been implemented with the Iris data set and its validity and effectiveness is tested with the help of commonly used internal evaluation measures for clustering like Davies Boudlin Index and Dunn Index.
5 citations
TL;DR: The optimal number of clusters for the experimental dataset have been concluded as K=2 and the optimal method for clustering the given dataset is hierarchical.
Abstract: Objective: This paper discusses and compares the various clustering methods over Ill-structured datasets and the primary objective is to find the best clustering method and to fix the optimal number of clusters. Methods: The dataset used in this experiment has derived from the measures of sensors used in an urban waste water treatment plant. In this paper, clustering methods like hierarchical, K means and PAM have been compared and internal cluster validity indices like connectivity, Dunn index, and silhouette index have been used to validate the clusters and the optimization of clustering is expressed in terms of number of clusters. At the end, experiment is done by varying the number of clusters and optimal scores are calculated. Findings: Optimal score and optimal rank list are generated which reveals that the hierarchical clustering is the optimal clustering method. The optimum value of connectivity index should be minimum, silhouette should be maximum, dunn should be maximum. So by interpreting the results, the optimal number of clusters for the experimental dataset have been concluded as K=2 and the optimal method for clustering the given dataset is hierarchical. Applications: The experiment has been done over the dataset derived from the measures of sensors used in a urban waste water treatment plant.
5 citations
30 Oct 2014
TL;DR: The proposed method clusters food offers based on the similarity between their nutritional features (e.g. calcium, vitamins etc.) and/or ingredients and the similarity is evaluated using the Sorensen-Dice coefficient.
Abstract: This paper presents a method for clustering food offers based on the cuckoo search algorithm. The proposed method clusters food offers based on the similarity between their nutritional features (e.g. calcium, vitamins etc.) and/or ingredients. The similarity is evaluated by using the Sorensen-Dice coefficient. To test the clustering method proposed here, we have developed in-house a set of 800 food offers. The food offers have been generated as starting from a set of food recipes (provided in an XML standard for sharing recipes) and a database containing information about nutritional features. This database stores the nutritional features of each food type, as provided by the Agricultural Research Service of the United States Department of Agriculture. We evaluated the performance of our clustering method by using the following metrics: the Dunn Index, the Davies-Bouldin index, and the Average Item-Cluster Similarity.
4 citations
31 Oct 2012
TL;DR: Among the data preprocessing methods, the data by factor analysis shows the best efficiency for clustering analysis, however, it is not enough to find the optimal cluster number.
Abstract: In this study, the data preprocessing methods were analyzed to obtain the optimal clustering solution in South Korea. The geographic data and weather data in 75 stations of Korea Meteorological Administration are applied. The applied data preprocessing methods are general normalization, modified normalization, standardization and factor analysis. After the clustering analysis were conducted by K-mean method with preprocessing data, the efficiency of data preprocessing methods are estimated using the clustering index, such as Dunn index and Silhouette index. The clustering analysis are carried out as the cluster number changes from 3 to 9. Among the data preprocessing methods, the data by factor analysis shows the best efficiency for clustering analysis. However, it is not enough to find the optimal cluster number.
4 citations
TL;DR: Clustering based on customers’ TWP instead of TDP makes it easier to find customers which have equipment turned on during Saturdays and the number of clusters affects the Davies-Bouldin Index and the Dunn Index more than the temporal resolution of data.
4 citations