International Association of Engineering and Management Education (IAEME)
K-means clustering algorithm is a heuristic algorithm that partitions the dataset into k clusters by minimizing the sum of squared distance in each cluster. In contrast, there are number of weaknesses. First it requires a prior knowledge of cluster number 'K'. Second it is sensitive to initialization which leads to random solutions. This paper presents a new approach to k-means clustering by providing a solution to initial selection of cluster centroids and a dynamic approach based on silhouette validity index.