Abstract :
The following topics are dealt with: data mining; local clustering; spatiotemporal event detection; time series; Markov models; email classification; data stream; parallel mining; Bayesian network; unsupervised learning; missing values prediction; anomaly detection; decision tree; binary classifier; data similarity matrix; data mapping; support vector machine; Mapreduce; document similarity; social network; text mining; Website; kernel learning; collaborative filtering; spectral transformation; graph visualization; and knowledge discovery.
Keywords :
Markov processes; belief networks; data mining; data visualisation; decision trees; electronic mail; learning (artificial intelligence); pattern classification; pattern clustering; spatiotemporal phenomena; support vector machines; text analysis; time series; Bayesian network; Mapreduce; Markov model; Website; anomaly detection; binary classifier; collaborative filtering; data mapping; data mining; data similarity matrix; data stream; decision tree; document similarity; email classification; graph visualization; kernel learning; knowledge discovery; local clustering; missing values prediction; parallel mining; social network; spatiotemporal event detection; spectral transformation; support vector machine; text mining; time series; unsupervised learning;
Conference_Titel :
Data Mining (ICDM), 2010 IEEE 10th International Conference on
Conference_Location :
Sydney, NSW
Print_ISBN :
978-1-4244-9131-5
DOI :
10.1109/ICDM.2010.1
