Dynamic Approach to K-Means Clustering Algorithm

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Provided by: International Association of Engineering and Management Education (IAEME)
Topic: Big Data
Format: PDF
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.
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