Dynamic Incremental K-means Clustering
Bryant Aaron, Dan E. Tamir, Naphtali David Rishe, Abraham Kandel · 2014
K-means clustering is one of the most commonly used methods for classification and data-mining. When the amount of data to be clustered is "huge," and/or when data becomes available in increments, one has to devise incremental K-means procedures. Current research on incremental clustering does not address several of the specific problems of incremental K-means including the seeding problem, sensitivity of the algorithm to the order of the data, and the number of clusters. In this paper we present static and dynamic single-pass incremental K-means procedures that overcome these limitations.