Probabilistic frequent itemset mining in uncertain transaction databases semantically and computationally differs from traditional techniques applied to standard "certain&quo...
Correlated or discriminative pattern mining is concerned with finding the highest scoring patterns w.r.t. a correlation measure (such as information gain). By reinterpreting corre...
Mining frequent closed itemsets provides complete and condensed information for non-redundant association rules generation. Extensive studies have been done on mining frequent clo...
We introduce a novel framework (BLOSOM) for mining (frequent) boolean expressions over binary-valued datasets. We organize the space of boolean expressions into four categories: p...
Lizhuang Zhao, Mohammed J. Zaki, Naren Ramakrishna...
Projected clustering has become a hot research topic due to its ability to cluster high-dimensional data. However, most existing projected clustering algorithms depend on some cri...