A new clustering algorithm
Xinbin Yang, Dao Zheng Huang · 2004
Clustering analysis is an important part of the data mining community. Traditional clustering algorithm is slow in convergence and sensitive to the initial value and preset classed in large scale data set. Ant colony algorithm is a kind of evolutionary algorithm with global optimization quality to deal with discrete problems. The ant colony algorithm is applied in aggregation analysis for the first time in this paper. A new clustering algorithm is presented based on the ant colony algorithm. This algorithm has the qualities of essential parallel, quick convergence and high effectiveness. The experimental result shows that it is about 10% higher than the C-means method in effectiveness.