A New Line Symmetry Distance and Its Application to Data Clustering  

作　　者：Sriparna Saha Sanghamitra Bandyopadhyay 

机构地区：[1]Machine Intelligence Unit,Indian Statistical Institute,Kolkata,India

出　　处：《Journal of Computer Science & Technology》2009年第3期544-556,共13页计算机科学技术学报（英文版）

摘　　要：In this paper, at first a new line-symmetry-based distance is proposed. The properties of the proposed distance are then elaborately described. Kd-tree-based nearest neighbor search is used to reduce the complexity of computing the proposed line-symmetry-based distance. Thereafter an evolutionary clustering technique is developed that uses the new linesymmetry-based distance measure for assigning points to different clusters. Adaptive mutation and crossover probabilities are used to accelerate the proposed clustering technique. The proposed GA with line-symmetry-distance-based （GALSD） clustering technique is able to detect any type of clusters, irrespective of their geometrical shape and overlapping nature, as long as they possess the characteristics of line symmetry. GALSD is compared with the existing well-known K-means clustering algorithm and a newly developed genetic point-symmetry-distance-based clustering technique （GAPS） for three artificial and two real-life data sets. The efficacy of the proposed line-symmetry-based distance is then shown in recognizing human face from a given image.In this paper, at first a new line-symmetry-based distance is proposed. The properties of the proposed distance are then elaborately described. Kd-tree-based nearest neighbor search is used to reduce the complexity of computing the proposed line-symmetry-based distance. Thereafter an evolutionary clustering technique is developed that uses the new linesymmetry-based distance measure for assigning points to different clusters. Adaptive mutation and crossover probabilities are used to accelerate the proposed clustering technique. The proposed GA with line-symmetry-distance-based （GALSD） clustering technique is able to detect any type of clusters, irrespective of their geometrical shape and overlapping nature, as long as they possess the characteristics of line symmetry. GALSD is compared with the existing well-known K-means clustering algorithm and a newly developed genetic point-symmetry-distance-based clustering technique （GAPS） for three artificial and two real-life data sets. The efficacy of the proposed line-symmetry-based distance is then shown in recognizing human face from a given image.

关 键 词：unsupervised classification CLUSTERING symmetry property line-symmetry-based distance KD-TREE genetic algorithm face recognition 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论]