Improving clustering efficiency by SimHash-based K-Means algorithm for big data analytics
Jenq‐Haur Wang, Jia-Zhi Lin · 2016
K-Means algorithm is one of the most popular methods for flat clustering, but it's time-consuming in similarity calculation for big data, which causes lower performance in practice. Previous studies proposed improvements for finding better initial centroids to facilitate effective assignment of the data points to suitable clusters with reduced time complexity. However, in vector space representation, as the data volume increases, the dimension of vector space becomes higher which takes more time in similarity calculation. In this paper, we propose a SimHash-based K-Means clustering algorithm that used locality-sensitive hashing and dimensionality reduction to improve the efficiency in big data analytics. The experimental results showed that our proposed method greatly reduces the processing time of K-Means clustering without significantly affecting the effectiveness. Further investigation is needed to verify the performance for data in larger scale.