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

Data clustering is used regularly in many applications such as data mining, vector quantization, pattern recognition, and fault detection & speaker recognition. The most well-known, widely used and fast methods for clustering is K-means clustering developed by Mac Queen in 1967. The simplicity of K-means clustering made this algorithm used in various fields. K-means clustering is a partitioning clustering method that separates data into k mutually groups. Through such the iterative partitioning, K-means clustering minimizes the sum of distance from each data to its clusters. (5) K-means is simple and can be easily used f