OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Data bubbles: quality preserving performance boosting for hierarchical clustering
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Mining data streams under block evolution
ACM SIGKDD Explorations Newsletter
Requirements for clustering data streams
ACM SIGKDD Explorations Newsletter
The X-tree: An Index Structure for High-Dimensional Data
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
An Incremental Hierarchical Data Clustering Algorithm Based on Gravity Theory
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
An Incremental Approach to Building a Cluster Hierarchy
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Extracting Delta for Incremental Data Warehouse Maintenance
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Incremental and effective data summarization for dynamic hierarchical clustering
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Data bubbles for non-vector data: speeding-up hierarchical clustering in arbitrary metric spaces
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Automatic extraction of clusters from hierarchical clustering representations
PAKDD'03 Proceedings of the 7th Pacific-Asia conference on Advances in knowledge discovery and data mining
Sequential Hierarchical Pattern Clustering
PRIB '09 Proceedings of the 4th IAPR International Conference on Pattern Recognition in Bioinformatics
Nearest Neighbor-Based Classification of Uncertain Data
ACM Transactions on Knowledge Discovery from Data (TKDD)
Towards never-ending learning from time series streams
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
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Many important industrial applications rely on data mining methods to uncover patterns and trends in large data warehouse environments. Since a data warehouse is typically updated periodically in a batch mode, the mined patterns have to be updated as well. This requires not only accuracy from data mining methods but also fast availability of up-to-date knowledge, particularly in the presence of a heavy update load. To cope with this problem, we propose the use of online data mining algorithms which permanently store the discovered knowledge in suitable data structures and enable an efficient adaptation of these structures after insertions and deletions on the raw data. In this paper, we demonstrate how hierarchical clustering methods can be reformulated as online algorithms based on the hierarchical clustering method OPTICS, using a density estimator for data grouping. We also discuss how this algorithmic schema can be specialized for efficient online single-link clustering. A broad experimental evaluation demonstrates that the efficiency is superior with significant speed-up factors even for large bulk insertions and deletions.