A Fast Global k-means Algorithm for Datasets having Streaming Behavior - Das, Purnendu; Ranjan Roy, Bishwa; Das, Sanju (International Journal of Computer Sciences and Engineering, 2025)
The k-means algorithm and its variations are known to be fast clustering algorithms. However, they are sensitive to the choice of starting points and are inefficient for solving clustering problems in large datasets. Recently, incremental approaches have been developed to resolve difficulties with the choice of starting points. The global k-means and the fast global k-means algorithms are based on such an approach. They iteratively add one cluster center at a time. Numerical experiments show that these algorithms considerably improve the k-means algorithm. However, they require storing the whole affinity matrix or computing this matrix at each iteration. This makes both algorithms time consuming and memory demanding for clustering even moderately large datasets. Also the continuously arriving data stream has become common phenomenon for many fields recent years; for example, sensor networks, web click stream and internet traffic flow. Researchers proposes many innovative technologies to manage such streaming datasets. Finding efficient data stream mining algorithm has become an important research subject. In this paper we propose a fast global k-means algorithm for datasets having streaming behavior. Experiment shows that our proposed algorithm is more efficient than the fast global k-means algorithm in case of streaming datasets.