A New Approach to Online Generation of Association Rules
IEEE Transactions on Knowledge and Data Engineering
Mining Associations with the Collective Strength Approach
IEEE Transactions on Knowledge and Data Engineering
Redefining Clustering for High-Dimensional Applications
IEEE Transactions on Knowledge and Data Engineering
Fast Algorithms for Online Generation of Profile Association Rules
IEEE Transactions on Knowledge and Data Engineering
Intelligent healthcare data analysis using statistic data miner
ACOS'06 Proceedings of the 5th WSEAS international conference on Applied computer science
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We have developed a distributed data mining algorithm based on the progressive knowledge extraction principle. The knowledge factors, the data attributes that are significant statistically or based on a predefined mining function, are extracted progressively from the distributed data sets. The critical data attributes and sample data set are selected iteratively from distributed data sources. The experiments showed that the algorithm is valid and has the potentials for the large distributed data mining practices.