Fast subsequence matching in time-series databases
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Data mining: concepts and techniques
Data mining: concepts and techniques
Clustering Data Streams: Theory and Practice
IEEE Transactions on Knowledge and Data Engineering
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
Efficient Time Series Matching by Wavelets
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
A symbolic representation of time series, with implications for streaming algorithms
DMKD '03 Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Clustering of Time Series Subsequences is Meaningless: Implications for Previous and Future Research
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Clustering Time Series with Clipped Data
Machine Learning
A framework for clustering evolving data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
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Clustering streaming time series is a difficult problem. Most traditional algorithms are too inefficient for large amounts of data and outliers in them. In this paper, we propose a new clustering method, which clusters Bi-clipped (CBC) stream data. It contains three phrases, namely, dimensionality reduction through piecewise aggregate approximation (PAA), Bi-clipped process that clipped the real valued series through bisecting the value field, and clustering. Through related experiments, we find that CBC gains higher quality solutions in less time compared with M-clipped method that clipped the real value series through the mean of them, and unclipped methods. This situation is especially distinct when streaming time series contain outliers.