scholarly journals New internal clustering validation measure for contiguous arbitrary‐shape clusters

Author(s):  
Juan Carlos Rojas‐Thomas ◽  
Matilde Santos
2017 ◽  
Vol 29 (11) ◽  
pp. 3094-3117 ◽  
Author(s):  
Jie Yang ◽  
Yan Ma ◽  
Xiangfen Zhang ◽  
Shunbao Li ◽  
Yuping Zhang

The traditional [Formula: see text]-means algorithm has been widely used as a simple and efficient clustering method. However, the performance of this algorithm is highly dependent on the selection of initial cluster centers. Therefore, the method adopted for choosing initial cluster centers is extremely important. In this letter, we redefine the density of points according to the number of its neighbors, as well as the distance between points and their neighbors. In addition, we define a new distance measure that considers both Euclidean distance and density. Based on that, we propose an algorithm for selecting initial cluster centers that can dynamically adjust the weighting parameter. Furthermore, we propose a new internal clustering validation measure, the clustering validation index based on the neighbors (CVN), which can be exploited to select the optimal result among multiple clustering results. Experimental results show that the proposed algorithm outperforms existing initialization methods on real-world data sets and demonstrates the adaptability of the proposed algorithm to data sets with various characteristics.


2013 ◽  
Vol E96.B (7) ◽  
pp. 2014-2023 ◽  
Author(s):  
Ryo YAMAGUCHI ◽  
Shouhei KIDERA ◽  
Tetsuo KIRIMOTO

1997 ◽  
Vol 33 (18) ◽  
pp. 1512 ◽  
Author(s):  
L. Landesa ◽  
F. Obelleiro ◽  
J.L. Rodríguez ◽  
A.G. Pino

1966 ◽  
Vol 16 (1) ◽  
pp. 273-300 ◽  
Author(s):  
Richard M. Sonshine ◽  
Raymond G. Cox ◽  
Howard Brenner

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