SVD-initialised K-means clustering for collaborative filtering recommender systems
by Murchhana Tripathy; Santilata Champati; Srikanta Patnaik
International Journal of Management and Decision Making (IJMDM), Vol. 21, No. 1, 2022

Abstract: K-means is a popular partitional clustering algorithm used by collaborative filtering recommender systems. However, the clustering quality depends on the value of K and the initial centroid points and consequently research efforts have instituted many new methods and algorithms to address this problem. Singular value decomposition (SVD) is a popular matrix factorisation technique that can discover natural clusters in a data matrix. We use this potential of SVD to solve the K-means initialisation problem. After finding the clusters, they are further refined by using the rank of the matrix and the within-cluster distance. The use of SVD based initialisation for K-means helps to retain the cluster quality and the cluster initialisation process gets automated.

Online publication date: Thu, 09-Dec-2021

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