Storage and retrieval considerations of binary data bases
Information Processing and Management: an International Journal
Algorithms for clustering data
Algorithms for clustering data
Bitmap index design and evaluation
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
ACM Computing Surveys (CSUR)
Hempel's raven paradox: a positive approach to cluster analysis
Computers and Operations Research
Data Mining by Means of Binary Representation: A Model for Similarity and Clustering
Information Systems Frontiers
Model 204 Architecture and Performance
Proceedings of the 2nd International Workshop on High Performance Transaction Systems
Performance Measurements of Compressed Bitmap Indices
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Fast k-Nearest Neighbor Classification Using Cluster-Based Trees
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast and Robust General Purpose Clustering Algorithms
Data Mining and Knowledge Discovery
Investigating diversity of clustering methods: An empirical comparison
Data & Knowledge Engineering
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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.