Title :
A novel hybrid clustering based on adaptive ACO and PSO
Author :
Xiong, Wen ; Wang, Cong
Author_Institution :
Inst. of Chinese Inf. Process., Beijing Normal Univ., Beijing, China
Abstract :
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 A-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.
Keywords :
data mining; particle swarm optimisation; pattern clustering; unsupervised learning; adaptive ACO; adaptive PSO; adaptive ant colony optimization; data mining; hybrid clustering approach; k-means method; particle swarm optimization; unsupervised machine learning method; Algorithm design and analysis; Ant colony optimization; Clustering algorithms; Data mining; Genetic algorithms; Optimization; Particle swarm optimization; ant colony optimization (ACO); clustering; data mining (DM); particle swarm optimization (PSO); swarm intelligence (SI);
Conference_Titel :
Computer Science and Service System (CSSS), 2011 International Conference on
Conference_Location :
Nanjing
Print_ISBN :
978-1-4244-9762-1
DOI :
10.1109/CSSS.2011.5975039