Synchronization-based clustering on evolving data stream

Author(s)

Junming Shao, Yue Tan, Lianli Gao, Qinli Yang, Claudia Plant, Ira Assent

Abstract

Clustering streams of data is of increasing importance in many applications. In this paper, we propose a new synchronization-based clustering approach for evolving data streams, called SyncTree, which maintains all micro-clusters at different levels of granularity depending upon the data recency. Instead of using a sliding window or decay function to focus on recent data, SyncTree summarizes all continuously-arriving objects as synchronized micro-clusters sequentially in a batch fashion. Owing to the powerful concept of synchronization, the derived micro-clusters truly reflect the intrinsic cluster structure rather than summarize statistics of data, and old micro-clusters can be intuitively summarized at a higher level by iterative clustering to fit memory constraints. Building upon the hierarchical micro-clusters, SyncTree allows investigating the cluster structure of the data stream between any two time stamps in the past, and also provides a principled way to analyze the cluster evolution. Empirical results demonstrate that our method has good performance compared to state-of-the-art algorithms.

Organisation(s)

Research Group Data Mining and Machine Learning

External organisation(s)

University of Electronic Science and Technology of China, Aarhus University

Journal

Information Sciences

Volume

501

Pages

573-587

No. of pages

15

ISSN

0020-0255

DOI

https://doi.org/10.1016/j.ins.2018.09.035

Publication date

09-2018

Peer reviewed

Yes

Austrian Fields of Science 2012

102033 Data mining

Keywords

ASJC Scopus subject areas

Software, Information Systems and Management, Artificial Intelligence, Theoretical Computer Science, Control and Systems Engineering, Computer Science Applications

Portal url

https://ucris.univie.ac.at/portal/en/publications/synchronizationbased-clustering-on-evolving-data-stream(0877e080-d7c0-4bfa-a3ba-3bca31584d3f).html