Semi-naive Bayesian classifiers seek to retain the numerous strengths of naive Bayes while reducing error by weakening the attribute independence assumption. Backwards Sequential ...
Real datasets are often large enough to necessitate data compression. Traditional `syntactic' data compression methods treat the table as a large byte string and operate at t...
H. V. Jagadish, Raymond T. Ng, Beng Chin Ooi, Anth...
This paper describes a system for the animation of algorithms on an electronic chalkboard. The instructor teaching an algorithm can enter data directly through a drawing- the algor...
Clustering is an important data mining problem. However, most earlier work on clustering focused on numeric attributes which have a natural ordering to their attribute values. Rec...
Ingcreasingly, data-mining algorithms must deal with databases that continuously grow over time. These algorithms must avoid repeatedly scanning their databases. When database att...