Abstract: The present invention is an apparatus and method for classifying high-dimensional sparse datasets. A raw data training set is flattened by converting it from categorical representation to a boolean representation. The flattened data is then used to build a class model on which new data not in the training set may be classified. In one embodiment, the class model takes the form of a decision tree, and large itemsets and cluster information are used as attributes for classification. In another embodiment, the class model is based on the nearest neighbors of the data to be classified. An advantage of the invention is that, by flattening the data, classification accuracy is increased by eliminating artificial ordering induced on the attributes. Another advantage is that the use of large itemsets and clustering increases classification accuracy.

Abstract: The present invention is directed to a data mining method and apparatus that dynamically initiates the counting of sets of items (itemsets) at any time during the pass over the records of a database and terminates the counting at the same location in the next pass. In this manner, the present invention begins to count itemsets early and finishes counting early while keeping the number of different itemsets which are being counted in any pass relatively low.

Abstract: The present invention discloses a data mining method and apparatus that assigns weight values to items and/or transactions based on the value to the user, thereby resulting in association rules of greater importance. A conservative method, aggressive method, or a combination of the two can be used when generating supersets.