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
OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
ACM Computing Surveys (CSUR)
Querying and mining data streams: you only get one look a tutorial
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
DEMON: Mining and Monitoring Evolving Data
IEEE Transactions on Knowledge and Data Engineering
Incremental Clustering for Mining in a Data Warehousing Environment
VLDB '98 Proceedings of the 24rd 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
Clustering Data Streams: Theory and Practice
IEEE Transactions on Knowledge and Data Engineering
Incremental Clustering and Dynamic Information Retrieval
SIAM Journal on Computing
Density Connected Clustering with Local Subspace Preferences
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
On Change Diagnosis in Evolving Data Streams
IEEE Transactions on Knowledge and Data Engineering
MONIC: modeling and monitoring cluster transitions
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
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
ACM Transactions on Knowledge Discovery from Data (TKDD)
Towards subspace clustering on dynamic data: an incremental version of PreDeCon
Proceedings of the First International Workshop on Novel Data Stream Pattern Mining Techniques
An incremental data stream clustering algorithm based on dense units detection
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
A survey on enhanced subspace clustering
Data Mining and Knowledge Discovery
Semi-supervised projected model-based clustering
Data Mining and Knowledge Discovery
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Modern data are often high dimensional and dynamic. Subspace clustering aims at finding the clusters and the dimensions of the high dimensional feature space where these clusters exist. So far, the subspace clustering methods are mainly static and cannot address the dynamic nature of modern data. In this paper, we propose a dynamic subspace clustering method, which extends the density based projected clustering algorithm PreDeCon for dynamic data. The proposed method efficiently examines only those clusters that might be affected due to the population update. Both single and batch updates are considered.