“Padding” bitmaps to support similarity and mining

The current paper presents a novel approach to bitmap-indexing for data mining purposes. Currently bitmap-indexing enables efficient data storage and retrieval, but is limited in terms of similarity measurement, and hence as regards classification, clustering and data mining. Bitmap-indexes mainly fit nominal discrete attributes and thus unattractive for widespread use, which requires the ability to handle continuous data in a raw format. The current research describes a scheme for representing ordinal and continuous data by applying the concept of “padding” where each discrete nominal data value is transformed into a range of nominal-discrete values. This "padding" is done by adding adjacent bits "around" the original value (bin). The padding factor, i.e., the number of adjacent bits added, is calculated from the first and second derivative degrees of each attribute’s domain-distribution. The padded representation better supports similarity measures, and therefore improves the accuracy of clustering and mining. The advantages of padding bitmaps are demonstrated on Fisher’s Iris dataset.