An Improved Clustering Algorithm based on Intelligent Computing

Clustering technology has received a lot of concern in many areas such as engineering, medicine, biology and data mining. Collecting data points is the purpose of clustering and the most common clustering technology is K-means algorithm. However, results of kmeans depend on the initial state and convergence to a local optimum is also its drawback. To overcome these drawbacks, many studies have been done on clustering. This paper works out an improved optimization algorithm based on intelligent computation, which is called artificial fish swarm. The results of experiments show that the algorithm has certain superiority on speed of execution and clustering accuracy compared with the traditional k-means algorithm.