BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Automatic subspace clustering of high dimensional data for data mining applications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Models and issues in data stream systems
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Continually evaluating similarity-based pattern queries on a streaming time series
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Efficient Similarity Search In Sequence Databases
FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
On the need for time series data mining benchmarks: a survey and empirical demonstration
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
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
Streaming-Data Algorithms for High-Quality Clustering
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Clustering binary data streams with K-means
DMKD '03 Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Mining concept-drifting data streams using ensemble classifiers
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Statistical grid-based clustering over data streams
ACM SIGMOD Record
Measuring correlation between microarray time-series data using dominant spectral component
APBC '04 Proceedings of the second conference on Asia-Pacific bioinformatics - Volume 29
Information Systems - Databases: Creation, management and utilization
On demand classification of data streams
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Exact indexing of dynamic time warping
Knowledge and Information Systems
BRAID: stream mining through group lag correlations
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Online clustering of parallel data streams
Data & Knowledge Engineering
Adaptive Clustering for Multiple Evolving Streams
IEEE Transactions on Knowledge and Data Engineering
StatStream: statistical monitoring of thousands of data streams in real time
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Extensions of vector quantization for incremental clustering
Pattern Recognition
A framework for clustering evolving data streams
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
GAKREM: A novel hybrid clustering algorithm
Information Sciences: an International Journal
Clustering high dimensional data: A graph-based relaxed optimization approach
Information Sciences: an International Journal
Information Sciences: an International Journal
Information Sciences: an International Journal
Performance evaluation of density-based clustering methods
Information Sciences: an International Journal
A method of relational fuzzy clustering based on producing feature vectors using FastMap
Information Sciences: an International Journal
Towards supporting expert evaluation of clustering results using a data mining process model
Information Sciences: an International Journal
Anomaly intrusion detection by clustering transactional audit streams in a host computer
Information Sciences: an International Journal
Validation of overlapping clustering: A random clustering perspective
Information Sciences: an International Journal
A framework for clustering categorical time-evolving data
IEEE Transactions on Fuzzy Systems
Efficient matching and retrieval of gene expression time series data based on spectral information
ICCSA'05 Proceedings of the 2005 international conference on Computational Science and Its Applications - Volume Part III
SMART: Stream Monitoring enterprise Activities by RFID Tags
Information Sciences: an International Journal
Mining frequent patterns in a varying-size sliding window of online transactional data streams
Information Sciences: an International Journal
From model-based control to data-driven control: Survey, classification and perspective
Information Sciences: an International Journal
Clustering local frequency items in multiple databases
Information Sciences: an International Journal
Fuzzy partition based soft subspace clustering and its applications in high dimensional data
Information Sciences: an International Journal
Uncovering overlapping cluster structures via stochastic competitive learning
Information Sciences: an International Journal
Information Sciences: an International Journal
Proceedings of the Second International Conference on Innovative Computing and Cloud Computing
Mining top-k frequent patterns over data streams sliding window
Journal of Intelligent Information Systems
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We propose a new algorithm to cluster multiple and parallel data streams using spectral component similarity analysis, a new similarity metric. This new algorithm can effectively cluster data streams that show similar behaviour to each other but with unknown time delays. The algorithm performs auto-regressive modelling to measure the lag correlation between the data streams and uses it as the distance metric for clustering. The algorithm uses a sliding window model to continuously report the most recent clustering results and to dynamically adjust the number of clusters. Our experimental results on real and synthetic datasets show that our algorithm has better clustering quality, efficiency, and stability than other existing methods.