An integrated approach for scaling up classification and prediction algorithms for data mining
SAICSIT '02 Proceedings of the 2002 annual research conference of the South African institute of computer scientists and information technologists on Enablement through technology
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AusDM '06 Proceedings of the fifth Australasian conference on Data mining and analystics - Volume 61
Domain ontology driven data mining: a medical case study
Proceedings of the 2007 international workshop on Domain driven data mining
Toward knowledge-driven data mining
Proceedings of the 2007 international workshop on Domain driven data mining
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Information Technology and Management
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This paper accentuate an approach of implementing Distributed Data Mining (DDM) using Multi-Agent System (MAS) technology, and proposes a data mining technique of “CAKE” (Classifying, Associating & Knowledge DiscovEry). The architecture is based on centralized PArallel Data Mining Agents (PADMAs). Data Mining is part of a word, which has been recently introduced known as BI or Business Intelligence. The need is to derive knowledge out of the abstract data. The process is difficult, complex, time consuming and resource starving. These highlighted problems addressed in the proposed model. The model architecture is distributed, uses knowledge-driven mining technique and flexible enough to work on any data warehouse, which will help to overcome these problems. Good knowledge of data, meta-data and business domain is required for defining rules for data mining. Taking into consideration that the data and data warehouse has already gone through the necessary processes and ready for data mining.