Fast algorithms for projected clustering
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Identifying distinctive subsequences in multivariate time series by clustering
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
A Monte Carlo algorithm for fast projective clustering
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Discovering Similar Multidimensional Trajectories
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Frequent-Pattern based Iterative Projected Clustering
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Mining coherent gene clusters from gene-sample-time microarray data
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
TRICLUSTER: an effective algorithm for mining coherent clusters in 3D microarray data
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Structure-Based Statistical Features and Multivariate Time Series Clustering
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Proceedings of the VLDB Endowment
ACM Transactions on Knowledge Discovery from Data (TKDD)
Evaluating clustering in subspace projections of high dimensional data
Proceedings of the VLDB Endowment
Clustering of time series data-a survey
Pattern Recognition
A review on time series data mining
Engineering Applications of Artificial Intelligence
Algorithm for Discovering Low-Variance 3-Clusters from Real-Valued Datasets
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Discovering Correlated Subspace Clusters in 3D Continuous-Valued Data
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
An effective evaluation measure for clustering on evolving data streams
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
External evaluation measures for subspace clustering
Proceedings of the 20th ACM international conference on Information and knowledge management
BioDM'06 Proceedings of the 2006 international conference on Data Mining for Biomedical Applications
Independent component analysis for clustering multivariate time series data
ADMA'05 Proceedings of the First international conference on Advanced Data Mining and Applications
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Mining temporal multivariate data by clustering techniques is recently gaining importance. However, the temporal data obtained in many of today's applications is often complex in the sense that interesting patterns are neither bound to the whole dimensional nor temporal extent of the data domain. Under these conditions, patterns mined by existing multivariate time series clustering and temporal subspace clustering techniques cannot correctly reflect the true patterns in the data. In this paper, we propose a novel clustering method that mines temporal coherent subspace clusters. In our model, these clusters are reflected by sets of objects and relevant intervals. Relevant intervals indicate those points in time in which the clustered time series show a high similarity. In our model, each dimension has an individual set of relevant intervals, which together ensure temporal coherence. In the experimental evaluation we demonstrate the effectiveness of our method in comparison to related approaches.