Fast Streaming $k$k-Means Clustering With Coreset Caching

We present new algorithms for
$k$
-means clustering on a data stream with a focus on providing fast responses to clustering queries. Compared to the state-of-the-art, our algorithms provide substantial improvements in the query time for cluster-center queries while retaining the desirable properties of provably small approximation error and low space usage. Our proposed clustering algorithms systematically reuse the “coresets” (summaries of data) computed for recent queries in answering the current clustering query, a novel technique which we refer to as coreset caching. We also present an algorithm called
OnlineCC
that integrates the coreset caching idea with a simple sequential streaming
$k$
-means algorithm. In practice,
OnlineCC
algorithm can provide constant query time. We present both theoretical analysis and detailed experiments demonstrating the correctness, accuracy, and efficiency of all our proposed clustering algorithms.