A Genetic Algorithm Based Clustering Using Geodesic Distance Measure
Aim at the problem that classical Euclidean distance metric cannot generate a appropriate partition for data lying in a manifold, a genetic algorithm based clustering method using geodesic distance measure is put forward. In this study, a prototype-based genetic representation is utilized, where each chromosome is a sequence of positive integer numbers that represent the k-medoids. Additionally, a geodesic distance based proximity measures is adopted to measure the similarity among data points. Experimental results on eight benchmark synthetic datasets with different manifold structure demonstrate the effectiveness of the algorithm as a clustering technique. Compared with generic K-means algorithm for clustering task, the presented algorithm has the ability to identify complicated non-convex clusters and its clustering performance is clearly better than that of the Kmeans algorithm for complex manifold structures.
genetic algorithm K-medoides geodesic distance data clustering
Gang Li Jian Zhuang Hongning Hou Dehong Yu
School of Mechanical Engineering Xian Jiaotong University Xian 710049,China
国际会议
上海
英文
274-278
2009-11-20（万方平台首次上网日期，不代表论文的发表时间）