Abstract :
The following topics are dealt with: pattern discovery; sparse graph mining; semisupervised feature importance evaluation; correlation clustering; support vector machine; unordered tree mining; unsupervised feature selection; directed graph; pattern mining; product classification; Markov logic network learning; recommendation system; Twitter; probabilistic modeling; hierarchical sparse representation; social network data; text categorization; stochastic Kronecker graph; Web-based partial order ranking; recursive multistep time series forecasting; document clustering; Bayesian network learning algorithm; missing data recovery; entropy-based graph clustering; context-aware multiinstance learning; and graph based online store review.
Keywords :
Internet; Markov processes; data mining; directed graphs; learning (artificial intelligence); pattern classification; pattern clustering; recommender systems; social networking (online); support vector machines; text analysis; time series; Bayesian network learning algorithm; Markov logic network learning; Twitter; Web-based partial order ranking; context-aware multiinstance learning; correlation clustering; directed graph; document clustering; entropy-based graph clustering; graph based online store review; hierarchical sparse representation; missing data recovery; pattern discovery; pattern mining; probabilistic modeling; product classification; recommendation system; recursive multistep time series forecasting; semisupervised feature importance evaluation; social network data; sparse graph mining; stochastic Kronecker graph; support vector machine; text categorization; unordered tree mining; unsupervised feature selection;
Conference_Titel :
Data Mining (ICDM), 2011 IEEE 11th International Conference on
Conference_Location :
Vancouver,BC
Print_ISBN :
978-1-4577-2075-8
DOI :
10.1109/ICDM.2011.7
