Abstract: Systems and methods of classifying a subject data item based on a training set of pre-classified data items. A piecewise-linear approximation of a local boundary between different classes of data items is automatically computed. The local boundary is approximated by a neighborhood set of data items selected from the training set that have been pre-classified into different classes and have points similar to a point of the subject data item. A class is automatically assigned to the subject data item in accordance with a side of the local boundary on which the subject data item resides.

Abstract: Systems and methods for performing data mining in a set of binary data arranged as a plurality of data items in which each data item has a plurality of bits, each bit in a corresponding one of a plurality of bit positions. The set of binary data is arranged in the data storage such that the binary data is in bit position groups. Each bit position group corresponds to a different one of the plurality of bit positions and includes bits of the binary data having that bit position. The binary data of each bit position group is compressed to produce data structures representing the set of binary data. A data mining technique is performed using the plurality of compressed data structures.