Frequent itemset mining (FIM) is an essential part of association rules mining. Its application for other data mining tasks has also been recognized. It has been an active researc...
We describe an efficient framework for Web personalization based on sequential and non-sequential pattern discovery from usage data. Our experimental results performed on real us...
Bamshad Mobasher, Honghua Dai, Tao Luo, Miki Nakag...
We present parallel algorithms for building decision-tree classifiers on shared-memory multiprocessor (SMP) systems. The proposed algorithms span the gamut of data and task parall...
Mohammed Javeed Zaki, Ching-Tien Ho, Rakesh Agrawa...
In order to adapt to time-varying wireless channels, various channel-adaptive schemes have been proposed to exploit inherent spatial diversity in mobile/wireless ad hoc networks w...
All methods of association rule mining require the frequent sets of items, that occur together sufficiently often to be the basis of potentially interesting rules, to be first com...