A novel hybrid clustering based on adaptive ACO and PSO

Clustering is an unsupervised machine learning method, which groups data into classes without labeled samples, and an important task in data mining. To attack the local optimum of k-means method, the paper presents a novel hybrid clustering approach, which uses adaptive ant colony optimization (ACO) to optimize the partition of data set, and utilizes enhanced particle swarm optimization (PSO) to refine the result of the adaptive ACO. Experiments displayed that the approach obtains smaller clustering evaluations on three data sets of University of California Irvine (UCI) and competitive results on two data sets of UCI, which verifying its availability.