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://ucrisportal.univie.ac.at/en/publications/0877e080-d7c0-4bfa-a3ba-3bca31584d3f