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 .