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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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10 Aug 2020
TL;DR: In this paper, cycle based clustering technique using reversible cellular automata (CAs) where closeness among objects is represented as objects belonging to the same cycle, that is reachable from each other.
Abstract: This work proposes cycle based clustering technique using reversible cellular automata (CAs) where ‘closeness’ among objects is represented as objects belonging to the same cycle, that is reachable from each other. The properties of such CAs are exploited for grouping the objects with minimum intra-cluster distance while ensuring that limited number of cycles exist in the configuration-space. The proposed algorithm follows an iterative strategy where the clusters with closely reachable objects of previous level are merged in the present level using an unique auxiliary CA. Finally, it is observed that, our algorithm is at least at par with the best algorithm existing today.
26 Sep 2020
TL;DR: This article argues a method for community partition based on information granularity, which optimize the social relationship model by using the link prediction method and establish the similarity model of user social relationship and obtains better I index, and Dunn index evaluation results compared with K-means.
Abstract: The social network community partition is conducive to obtaining hidden and valuable knowledge and rules, which is currently a hot research perspective. Traditional community mining often analyzes network structure information from a static point of view, but ignores the analysis of individual actors’ initiative, which limits the construction of community concept model and the effect of community partition. This article argues a method for community partition based on information granularity. First, we optimize the social relationship model by using the link prediction method and establish the similarity model of user social relationship. Second, aiming at the deficiency of K-means clustering algorithm and the defect of high dimension and sparsity of data, the principle of information granularity is introduced in user clustering analysis, and membership degree and generalized equivalence relation of user equivalence relation are given respectively. On this basis, we propose a social community partition method based on the information granularity. Finally, experiments show that, because of the effective integration of the important information of users’ social relations and the introduction of information granularity method, the proposed model obtains better I index, and Dunn index evaluation results compared with K-means.
01 Jan 2014
TL;DR: Microarray technology is one of the important biotechnological means that has made it possible to simultaneously monitor the expression levels of thousands of genes during important biological processes and across collections of related samples.
Abstract: Microarray technology is one of the important biotechnological means that has made it possible to simultaneously monitor the expression levels of thousands of genes during important biological processes and across collections of related samples [9, 14, 37].
08 Jun 2018
TL;DR: A new radial layout visualization, called the Quasi-circular mapping visualization (QCMV), is introduced to address the problems of ordering DAs and visual results of crowding which hamper clustering analysis.
Abstract: Radial coordinate visualization (RadViz) and Star Coordinates (SC) can effectively map high dimensional data to low dimensional space, owing to which can place an arbitrary number of Dimension Anchors (DAs) Nevertheless, the problem owner is faced with ordering DAs, which is a NP-complete problem and visual results of crowding which hamper clustering analysis We introduce a new radial layout visualization, called the Quasi-circular mapping visualization (QCMV), to address those problems in this paper Firstly, QCMV extend the original dimension of datasets by the probability distribution histogram of the dimension and affinity propagation (AP) algorithm In additional, distributing them on the unit circle by their correlation according to the correlation of the extended dimensions Then, mapping the dimensions extended and reordered data to integrate a polygon in the Quasi-circular space and visualizing them by the geometric center and area of the polygon in the three dimension Finally strengthening their visual clustering effect with t-SNE We also compare the visual clustering results of RadViz, SC and QCMV with two indexes, correct rate and Dunn index on visually analyzing the three datasets It shows better effect of visual clustering with QCMV
30 Apr 2020
Abstract: The earthquake is shocks or vibrations in the earth's surface because of shifting layers of rock at the base of the earth's surface. This natural phenomenon is common in Indonesia because it lies between Australian, Eurasian, Pacific plates, and it location surrounded by a ring of fire precisely. Therefore, this study aims to cluster earthquake events in Indonesia and describe the characteristics of each group based on clustering results. The method used is the Fuzzy K-Means Clustering. The clustering results obtained from clustering based on the depth, longitude, and latitude. In this study, the data used is the earthquake's data, which has a magnitude greater than or equal to 5 SR and only clumped by depth. Based on the Davies-Bouldin and Dunn index, the best clustering is 2 clusters which researchers cluster earthquake data in Indonesia into deep and shallow clusters.