Active Mining of Data Streams, SIAM Conference on Data Mining (SDM), 2004. ,
DOI : 10.1137/1.9781611972740.46
A Framework for Clustering Evolving Data Streams, Int. Conf. on Very Large Data Bases, pp.81-92, 2003. ,
DOI : 10.1016/B978-012722442-8/50016-1
Clustering data streams, IEEE Symposium on Foundations of Computer Science, pp.359-366, 2000. ,
Density-Based Clustering over an Evolving Data Stream with Noise, SIAM Conference on Data Mining (SDM, 2006. ,
DOI : 10.1137/1.9781611972764.29
Data Streams: Algorithms and Applications, Foundations and Trends?? in Theoretical Computer Science, vol.1, issue.2, pp.117-236, 2005. ,
DOI : 10.1561/0400000002
Adaptive, Hands-Off Stream Mining, Int. Conf. on Very Large Data Bases, pp.560-571, 2003. ,
DOI : 10.1016/B978-012722442-8/50056-2
Approximate counts and quantiles over sliding windows, Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems , PODS '04, pp.286-296, 2004. ,
DOI : 10.1145/1055558.1055598
Distributed topk monitoring, ACM International Conference on Management of Data, pp.28-39, 2003. ,
Clustering by Passing Messages Between Data Points, Science, vol.315, issue.5814, pp.972-976, 2007. ,
DOI : 10.1126/science.1136800
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.121.3145
Supporting online material of clustering by passing messages between data points, In: Science, vol.315, p.1, 2007. ,
Clustering data streams: theory and practice, IEEE Transactions on Knowledge and Data Engineering, vol.15, issue.3, pp.15-515, 2003. ,
DOI : 10.1109/TKDE.2003.1198387
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.108.9085
CONTINUOUS INSPECTION SCHEMES, Biometrika, vol.41, issue.1-2, pp.41-100, 1954. ,
DOI : 10.1093/biomet/41.1-2.100
Inference about the change-point from cumulative sum tests, Biometrika, vol.58, issue.3, pp.509-523, 1971. ,
DOI : 10.1093/biomet/58.3.509
Clustering by soft-constraint affinity propagation: applications to gene-expression data, Bioinformatics, vol.23, issue.20, p.2708, 2007. ,
DOI : 10.1093/bioinformatics/btm414
A density-based algorithm for discovering clusters in large spatial databases with noisethe uniqueness of a good optimum for k-means, International Conference on Knowledge Discovery and Data Mining(KDD, 1996. ,
The ucr time series classification/clustering homepage: www.cs.ucr.edu/ eamonn/time series data, KDD99: Kdd cup 1999 data, 1999. ,
A data mining framework for building intrusion detection models, IEEE Symposium on Security and Privacy, pp.120-132, 1999. ,
An error bound guarantee algorithm for online mining frequent sets over data streams, Journal of Knowledge and Information Systems, 2007. ,
Accurate decision trees for mining highspeed data streams, ACM International Conference on Management of Data, pp.523-528, 2003. ,
DOI : 10.1145/956804.956813
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.118.6092
Finding hierarchical heavy hitters in streaming data, ACM Transactions on Knowledge Discovery from Data, vol.1, issue.4, 2008. ,
DOI : 10.1145/1324172.1324174
An Empirical Bayes Approach to Detect Anomalies in Dynamic Multidimensional Arrays, Fifth IEEE International Conference on Data Mining (ICDM'05), 2005. ,
DOI : 10.1109/ICDM.2005.22