An EM-Based Algorithm for Clustering Data Streams in Sliding Windows

Authors:
Xuan Hong Dang;Vincent Lee;Wee Keong Ng;Arridhana Ciptadi;Kok Leong Ong
Affiliations:
Monash University, Australia;Monash University, Australia;Nanyang Technological University, Singapore;Nanyang Technological University, Singapore;Deakin University, Australia
Venue:
DASFAA '09 Proceedings of the 14th International Conference on Database Systems for Advanced Applications
Year:
2009

Citing 3
Cited 1

Models and issues in data stream systems

Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Density-based clustering for real-time stream data

Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
A framework for clustering evolving data streams

VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29

Data stream clustering: A survey

ACM Computing Surveys (CSUR)

Quantified Score

Hi-index	0.00

Visualization

Abstract

Cluster analysis has played a key role in data understanding. When such an important data mining task is extended to the context of data streams, it becomes more challenging since the data arrive at a mining system in one-pass manner. The problem is even more difficult when the clustering task is considered in a sliding window model which requiring the elimination of outdated data must be dealt with properly. We propose SWEM algorithm that exploits the Expectation Maximization technique to address these challenges. SWEM is not only able to process the stream in an incremental manner, but also capable to adapt to changes happened in the underlying stream distribution.