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)
Data mining: concepts and techniques
Data mining: concepts and techniques
Querying and mining data streams: you only get one look a tutorial
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
BIRCH: A New Data Clustering Algorithm and Its Applications
Data Mining and Knowledge Discovery
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
A generalized framework for mining spatio-temporal patterns in scientific data
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in 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)
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
Novel data stream pattern mining report on the StreamKDD'10 workshop
ACM SIGKDD Explorations Newsletter
Density based subspace clustering over dynamic data
SSDBM'11 Proceedings of the 23rd international conference on Scientific and statistical database management
Density-Based projected clustering of data streams
SUM'12 Proceedings of the 6th international conference on Scalable Uncertainty Management
Twitter spammer detection using data stream clustering
Information Sciences: an International Journal
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Todays data are high dimensional and dynamic, thus clustering over such kind of data is rather complicated. To deal with the high dimensionality problem, the subspace clustering research area has lately emerged that aims at finding clusters in subspaces of the original feature space. So far, the subspace clustering methods are mainly static and thus, cannot address the dynamic nature of modern data. In this paper, we propose an incremental version of the density based projected clustering algorithm PreDeCon, called incPreDeCon. The proposed algorithm efficiently updates only those subspace clusters that might be affected due to the population update.