A Study of Bio-Inspired Algorithm to Data Clustering Using Different Distance Measures
Data mining is the process of extracting previously unknown and valid information from large databases. Clustering is an important data analysis and data mining method. It is the unsupervised classification of objects into clusters such that the objects from same cluster are similar and objects from different clusters are dissimilar. Data clustering is a difficult unsupervised learning problem because many factors such as distance measures, criterion functions, and initial conditions have come into play. Many algorithms have been proposed in literature. However, some traditional algorithms have drawbacks such as sensitive to initialization and easily trapped in local optima. Recently, bio-inspired algorithms such as Ant COlony algorithms (ACO) and Particle Swarm Optimization algorithms (PSO) have found success in solving clustering problems.