文献类型：会议论文

英文题名：A new ant-based clustering algorithm on high dimensional data space

作者：Chen, Jianbin[1];Jie, Sun[1];Chen, Yunfei[1]

第一作者：陈建斌

通讯作者：Chen, JB[1]

机构：[1]Beijing Union Univ, Coll Business, Beijing 100025, Peoples R China

第一机构：北京联合大学商务学院

通讯机构：[1]corresponding author), Beijing Union Univ, Coll Business, Beijing 100025, Peoples R China.|[1141721]北京联合大学商务学院;[11417]北京联合大学;

会议论文集：14th International Conference on Concurrent Engineering

会议日期：2007

会议地点：Sao Paulo, BRAZIL

语种：英文

外文关键词：ant-based heuristic; clustering; high-dimensional data space

摘要：Ant-based clustering due to its flexibility, stigmergic and self-organization has been applied in a variety areas from problems arising in commerce, to circuit design, and to text-mining, etc. A new ant-based clustering method named AMC algorithm has been presented in this paper. Firstly, an artificial ant movement(AM) model is presented; secondly, the new ant clustering algorithm has been constructed based on AM model. In this algorithm, each ant is treated as an agent to represent a data object, each ant has two states: resting state and moving state. The ant's state is controlled by two predefined functions. By moving dynamically, the ants form different subgroups adaptively, and consequently the whole ant group dynamically self-organized into distinctive and independent subgroups within which highly similar ants are closely connected. This algorithm can be accelerated by the use of a global memory bank, increasing radius of perception and density-based 'look ahead' method for each agent. Experimental results show that the AMC algorithm is much superior to other ant clustering methods. It is adaptive, robust and efficient, and achieves high autonomy, simplicity and efficiency. It is suitable for solving high dimensional and complicated clustering problems.