Efficient Joint Clustering Algorithms in Optimization and Geography Domains

• Main Authors: Chia-Hao Lo, 駱嘉濠
• Other Authors: Wen-Chih Peng
• Format: Others
• Language: en_US
• Published: 2008
• Online Access: http://ndltd.ncl.edu.tw/handle/68401276653610303039

碩士 === 國立交通大學 === 資訊科學與工程研究所 === 96 === Prior works have elaborated on the problem of joint clustering in the optimization and geography domains. However, prior works neither clearly specify the connected constraint in the geography domain nor propose efficient algorithms. In this paper, we formulate the joint clustering problem in which a connected constraint and the number of clusters should be specified. We propose an algorithm K-means with Local Search (abbreviated as KLS) consisting of three phases: the transformation phase, the coarse clustering phase and the fine clustering phase. First, data objects that fulfill the connected constraint is represented as the ConGraph (standing for CONnected Graph). In light of the ConGraph, by adapting the concept of K-means and local search, an algorithm is devised to coarsely cluster objects for the purpose of efficiency. Then, these coarse cluster results are fine tuned to minimize the dissimilarity of the data objects in the optimization domain. Our experimental results show that KLS can find correct clusters efficiently.