A Survey on Optimization Approaches to K-Means Clustering using Simulated Annealing

Clustering is one of the fastest growing research areas because of availability of huge amount of data. It models data into the clusters. Data modelling puts clustering in a historical perspective rooted in statistics, mathematics, and numerical analysis. From a machine learning perception clusters correspond to hidden patterns, the exploration for clusters is unsupervised learning, the resultant system represents a data model. There are many techniques for clustering of data based on similarity. K-Means is one of the simplest unsupervised learning methods among all partitioning based clustering methods. It classifies a set of data objects in clusters. All the data objects are placed in a cluster having centroid nearest to that data object. After processing the data objects centroids are recalculated, and the whole process is repeated. This paper presents a brief estimation of the existing body of work that employs simulated annealing approach to improve upon the k-means clustering process.