Mining frequent closed itemsets provides complete and condensed information for non-redundant association rules generation. Extensive studies have been done on mining frequent clo...
Knowledge discovery systems are constrained by three main limited resources: time, memory and sample size. Sample size is traditionally the dominant limitation, but in many present...
Matrix factorization (MF) models have proved efficient and well scalable for collaborative filtering (CF) problems. Many researchers also present the probabilistic interpretation o...
This paper aims at discovering community structure in rich media social networks, through analysis of time-varying, multi-relational data. Community structure represents the laten...
Yu-Ru Lin, Jimeng Sun, Paul Castro, Ravi B. Konuru...
Mining informative patterns from very large, dynamically changing databases poses numerous interesting challenges. Data summarizations (e.g., data bubbles) have been proposed to c...