Representing and reasoning with probabilistic knowledge: a logical approach to probabilities
Representing and reasoning with probabilistic knowledge: a logical approach to probabilities
Mining for Strong Negative Associations in a Large Database of Customer Transactions
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
A methodology for hiding knowledge in databases
CRPIT '14 Proceedings of the IEEE international conference on Privacy, security and data mining - Volume 14
Itemset Trees for Targeted Association Querying
IEEE Transactions on Knowledge and Data Engineering
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One of the limitations of the techniques that have been developed as apart of the Knowledge Hiding in Databases (KHD) methodology is that they are not applicable to a general class of data mining algorithms. In this paper, we present a formal characterization of the KHD process for a general class of data mining algorithms, that we call concept-based. This particular class of mining algorithms includes decision-region based classification algorithms, association algorithms, negative association algorithms, and exception rule mining algorithms. All of these algorithms have the common feature that the patterns generated by them can be represented using Bacchus probability logic. Based on our concept of a pattern, each step of the KHD process is able to treat concept-based algorithms in a unified manner.