Performance Evaluation of Some Clustering Algorithms and Validity Indices
IEEE Transactions on Pattern Analysis and Machine Intelligence
A rough set paradigm for unifying rough set theory and fuzzy set theory
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
Rough set approach to domain knowledge approximation
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
A novel classification algorithm to noise data
ICSI'12 Proceedings of the Third international conference on Advances in Swarm Intelligence - Volume Part II
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As both rough sets theory and neural network in data mining have special advantages and exiting problems, this paper presented a combined algorithm based rough sets theory and BP neural network This algorithm deducts data from data warehouse by using rough sets' deduct function, and then moves the deducted data to the BP neural network as training data By data deduct, the expression of training will become clearer, and the scale of neural network can be simplified At the same time, neural network can easy up rough set's sensitivity for noise data This paper presents a cost function to express the relationship between the amount of training data and the precision of neural network, and to supply a standard for the change from rough set deduct to neural network training.