DISC: Density-Based Incremental Clustering by Striding over Streaming Data
Bogyeong Kim, Kyoseung Koo, Juhun Kim, Bongki Moon
Abstract
Given the prevalence of mobile and IoT devices, continuous clustering against streaming data has become an essential tool of increasing importance for data analytics. Among many clustering approaches, the density-based clustering has garnered much attention due to its unique advantages. The main drawback is, however, the limited scalability attributed to its relatively high computational cost, which is further aggravated when it has to update clusters continuously along with evolving data. In this paper, we present a new incremental density-based clustering algorithm called DISC optimized for the sliding window model. DISC is capable of producing exactly the same clustering results as existing methods such as Incremental DBSCAN for streaming data much more quickly and efficiently.