A course in density estimation
A course in density estimation
PODS '95 Proceedings of the fourteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
The KDD process for extracting useful knowledge from volumes of data
Communications of the ACM
Fast discovery of association rules
Advances in knowledge discovery and data mining
Efficient Similarity Search In Sequence Databases
FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
Online Generation of Association Rules
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Evaluation of sampling for data mining of association rules
RIDE '97 Proceedings of the 7th International Workshop on Research Issues in Data Engineering (RIDE '97) High Performance Database Management for Large-Scale Applications
New Algorithms for Fast Discovery of Association Rules
New Algorithms for Fast Discovery of Association Rules
Comparing Datasets Using Frequent Itemsets: Dependency on the Mining Parameters
SETN '08 Proceedings of the 5th Hellenic conference on Artificial Intelligence: Theories, Models and Applications
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In this paper we present an elegant and effective algorithm for measuring the similarity between homogeneous datasets to enable clustering. Once similar datasets are clustered, each cluster can be independently mined to generate the appropriate rules for a given cluster. The algorithm presented is efficient in storage and scale, has the ability to adjust to time constraints, and can provide the user with likely causes of similarity or dis-similarity. The proposed similarity measure is evaluated and validated on real datasets from the Census Bureau, Reuters, and synthetic datasets from IBM.