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SIGMOD
2004
ACM
209views Database» more  SIGMOD 2004»
14 years 7 months ago
MAIDS: Mining Alarming Incidents from Data Streams
Real-time surveillance systems, network and telecommunication systems, and other dynamic processes often generate tremendous (potentially infinite) volume of stream data. Effectiv...
Y. Dora Cai, David Clutter, Greg Pape, Jiawei Han,...
ICDM
2003
IEEE
119views Data Mining» more  ICDM 2003»
14 years 27 days ago
A Dynamic Adaptive Self-Organising Hybrid Model for Text Clustering
Clustering by document concepts is a powerful way of retrieving information from a large number of documents. This task in general does not make any assumption on the data distrib...
Chihli Hung, Stefan Wermter
NIPS
2008
13 years 9 months ago
Learning Taxonomies by Dependence Maximization
We introduce a family of unsupervised algorithms, numerical taxonomy clustering, to simultaneously cluster data, and to learn a taxonomy that encodes the relationship between the ...
Matthew B. Blaschko, Arthur Gretton
JCP
2006
111views more  JCP 2006»
13 years 7 months ago
Mining Developing Trends of Dynamic Spatiotemporal Data Streams
This paper1 presents an efficient modeling technique for data streams in a dynamic spatiotemporal environment and its suitability for mining developing trends. The streaming data a...
Yu Meng, Margaret H. Dunham
AUSDM
2007
Springer
112views Data Mining» more  AUSDM 2007»
14 years 1 months ago
Measuring Data-Driven Ontology Changes using Text Mining
Most current ontology management systems concentrate on detecting usage-driven changes and representing changes formally in order to maintain the consistency. In this paper, we pr...
Majigsuren Enkhsaikhan, Wilson Wong, Wei Liu, Mark...