Initialization of cluster refinement algorithms: a review and comparative study

• Published in: 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541) Vol. 1; pp. 297 - 302
• Main Authors: JI HE, MAN LAN, TAN, Chew-Lim, SUNG, Sam-Yuan, LOW, Hwee-Boon
• Format: Conference Proceeding
• Language: English
• Published: Piscataway NJ IEEE 2004
• Subjects: Applied sciences; Artificial intelligence; Clustering algorithms; Clustering methods; Computer science; control theory; systems; Data mining; Exact sciences and technology; Image segmentation; Iterative algorithms; Iterative methods; Optimization methods; Sampling methods; Sun; Unsupervised learning; Cluster analysis; Initialization; Density estimation; Local search; Density measurement; Random sampling; Refinement method; Optimization method; Iterative method; Global optimum; K means algorithm; Neural network; Modeling; Classification; NP hard problem; State dependence
• ISBN: 0780383591; 9780780383593
• ISSN: 1098-7576
• DOI: 10.1109/IJCNN.2004.1379917

Various iterative refinement clustering methods are dependent on the initial state of the model and are capable of obtaining one of their local optima only. Since the task of identifying the global optimization is NP-hard, the study of the initialization method towards a sub-optimization is of great value. This paper reviews the various cluster initialization methods in the literature by categorizing them into three major families, namely random sampling methods, distance optimization methods, and density estimation methods. In addition, using a set of quantitative measures, we assess their performance on a number of synthetic and real-life data sets. Our controlled benchmark identifies two distance optimization methods, namely SCS and KKZ, as complements of the k-means learning characteristics towards a better cluster separation in the output solution.