Storage and retrieval considerations of binary data bases
Information Processing and Management: an International Journal
Algorithms for clustering data
Algorithms for clustering data
Communications of the ACM
Data mining
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Bayesian classification (AutoClass): theory and results
Advances in knowledge discovery and data mining
From data mining to knowledge discovery: current challenges and future directions
Advances in knowledge discovery and data mining
Discovering data mining: from concept to implementation
Discovering data mining: from concept to implementation
Data mining (Invited talk. Abstract only): crossing the Chasm
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
ACM Computing Surveys (CSUR)
Hempel's raven paradox: a positive approach to cluster analysis
Computers and Operations Research
Clustering Algorithms
Computer
Journal of Management Information Systems - Special section: Data mining
Journal of Management Information Systems - Special section: Data mining
Short Term and Total Life Impact analysis of email worms in computer systems
Decision Support Systems
Visualization of multi-algorithm clustering for better economic decisions - The case of car pricing
Decision Support Systems
Classification by clustering decision tree-like classifier based on adjusted clusters
Expert Systems with Applications: An International Journal
Classification by clustering decision tree-like classifier based on adjusted clusters
Expert Systems with Applications: An International Journal
Experimental analysis of the q-matrix method in knowledge discovery
ISMIS'05 Proceedings of the 15th international conference on Foundations of Intelligent Systems
Proceedings of the 5th International Conference on PErvasive Technologies Related to Assistive Environments
"Padding" bitmaps to support similarity and mining
Information Systems Frontiers
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In this paper we outline a new method for clustering that is based on a binary representation of data records. The binary database relates each entity to all possible attribute values (domain) that entity may assume. The resulting binary matrix allows for similarity and clustering calculation by using the positive (‘1’ bits) of the entity vector. We formulate two indexes: Pair Similarity Index (PSI) to measure similarity between two entities and Group Similarity Index (GSI) to measure similarity within a group of entities. A threshold factor for each attribute domain is defined that is dependent on the domain but independent of the number of entities in the group. The similarity measure provides simplicity of storage and efficiency of calculation. A comparison of our similarity index to other indexes is made. Experiments with sample data indicate a 48% improvement of group similarity over standard methods pointing to the potential and merit of the binary approach to clustering and data mining.