Summarization and Matching of Density-Based Clusters in Streaming Environments

TL;DR

This paper introduces a novel multi-resolution summarization method for density-based clusters in streaming data, enabling real-time analysis and matching of complex cluster structures, which enhances cluster management and interpretation.

Contribution

It proposes Skeletal Grid Summarization (SGS) and C-SGS for real-time cluster summarization, along with an efficient cluster matching mechanism, advancing the state-of-the-art in streaming density-based clustering.

Findings

SGS effectively captures cluster shape and internal structure.

C-SGS enables real-time cluster summarization during streaming.

Proposed methods outperform alternatives in efficiency and effectiveness.

Abstract

Density-based cluster mining is known to serve a broad range of applications ranging from stock trade analysis to moving object monitoring. Although methods for efficient extraction of density-based clusters have been studied in the literature, the problem of summarizing and matching of such clusters with arbitrary shapes and complex cluster structures remains unsolved. Therefore, the goal of our work is to extend the state-of-art of density-based cluster mining in streams from cluster extraction only to now also support analysis and management of the extracted clusters. Our work solves three major technical challenges. First, we propose a novel multi-resolution cluster summarization method, called Skeletal Grid Summarization (SGS), which captures the key features of density-based clusters, covering both their external shape and internal cluster structures. Second, in order to summarize the extracted clusters in real-time, we present an integrated computation strategy C-SGS, which piggybacks the generation of cluster summarizations within the online clustering process. Lastly, we design a mechanism to efficiently execute cluster matching queries, which identify similar clusters for given cluster of analyst's interest from clusters extracted earlier in the stream history. Our experimental study using real streaming data shows the clear superiority of our proposed methods in both efficiency and effectiveness for cluster summarization and cluster matching queries to other potential alternatives.