Deterministic annealing EM algorithm
Neural Networks
Data mining: concepts and techniques
Data mining: concepts and techniques
Models and issues in data stream systems
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Sampling from a moving window over streaming data
SODA '02 Proceedings of the thirteenth annual ACM-SIAM symposium on Discrete algorithms
Maintaining stream statistics over sliding windows: (extended abstract)
SODA '02 Proceedings of the thirteenth annual ACM-SIAM symposium on Discrete algorithms
Querying and mining data streams: you only get one look a tutorial
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Maintaining variance and k-medians over data stream windows
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Clustering Data Streams: Theory and Practice
IEEE Transactions on Knowledge and Data Engineering
Better streaming algorithms for clustering problems
Proceedings of the thirty-fifth annual ACM symposium on Theory of computing
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
Density-based clustering for real-time stream data
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
StatStream: statistical monitoring of thousands of data streams in real time
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
A framework for clustering evolving data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Load shedding in a data stream manager
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
A framework for projected clustering of high dimensional data streams
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
SIC-means: a semi-fuzzy approach for clustering data streams using c-means
ANNPR'10 Proceedings of the 4th IAPR TC3 conference on Artificial Neural Networks in Pattern Recognition
| Hi-index | 0.00 |
Cluster analysis has played a key role in data stream understanding. The problem is difficult when the clustering task is considered in a sliding window model in which the requirement of outdated data elimination must be dealt with properly. We propose SWEM algorithm that is designed based on the Expectation Maximization technique to address these challenges. Equipped in SWEM is the capability to compute clusters incrementally using a small number of statistics summarized over the stream and the capability to adapt to the stream distribution's changes. The feasibility of SWEM has been verified via a number of experiments and we show that it is superior than Clustream algorithm, for both synthetic and real datasets.