Fast subsequence matching in time-series databases
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Locally adaptive dimensionality reduction for indexing large time series databases
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Sequential PAttern mining using a bitmap representation
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Motifs in Massive Time Series Databases
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
ROCK: A Robust Clustering Algorithm for Categorical Attributes
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
Mining Frequent Itemsets from Secondary Memory
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
MotifMiner: Efficient discovery of common substructures in biochemical molecules
Knowledge and Information Systems
CLICKS: an effective algorithm for mining subspace clusters in categorical datasets
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Multi-step density-based clustering
Knowledge and Information Systems
DUSC: Dimensionality Unbiased Subspace Clustering
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
A new multiobjective clustering technique based on the concepts of stability and symmetry
Knowledge and Information Systems
Integrating induction and deduction for noisy data mining
Information Sciences: an International Journal
Enhancing grid-density based clustering for high dimensional data
Journal of Systems and Software
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Many environmental, scientific, technical or medical database applications require effective and efficient mining of time series, sequences or trajectories of measurements taken at different time points and positions forming large temporal or spatial databases. Particularly the analysis of concurrent and multidimensional sequences poses new challenges in finding clusters of arbitrary length and varying number of attributes. We present a novel algorithm capable of finding parallel clusters in different subspaces and demonstrate our results for temporal and spatial applications. Our analysis of structural quality parameters in rivers is successfully used by hydrologists to develop measures for river quality improvements.