The new representation is more concise than two of the basic representations and more efficiently computable than the third representation.
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We describe three basic lossless representations of frequent patterns in a uniform way and offer a new lossless representation of frequent patterns based on disjunction-free generators. Therefore, it is important to work out concise representations of frequent itemsets. The number of frequent itemsets is usually huge. Frequent itemsets are useful in the discovery of association rules, episode rules, sequential patterns and clusters. son testing with well-known classification methods (like C4.5) indicate the superiority of the decomposition approachħF302FC1 International Conference on Data Mining marzena kryszkiewiczĒ001Ĝoncise representation of frequent patterns based on disjunction-free generators association rule + pattern classification + concise representation + frequent patterns + data mining + disjunction-free generators + association rules + sequential patterns + rule discovery + set theory + relational databases + lossless representations + computer science + clustering algorithms + very large databases + knowledge based systems + theorem proving + frequent pattern discovery + data mining problems + frequent itemsetsĚuthorProvided Keywords Not Found Many data mining problems require the discovery of frequent patterns in order to be solved.
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The results achieved in the empirical compart. A greedy procedure, called D-IFN, is developed to decompose the input attributes set into subsets and build a classification model for each subset separately. This paper presents theoretical and practical foundation for the attribute decomposition approach. A classification model is built for each subset, then all the models are combined using simple Bayesian combination. According to the attribute decomposition approach, the set of input attributes is automatically decomposed into several subsets. Paper_id venue authors year title index_keys author_keys abstractĨ11C1626 International Conference on Data Mining oded mainon + lior rokachĒ001 Theory and applications of attribute decomposition databases + pattern classification + attribute decomposition + records + data mining + testing + greedy procedure + classification model + bayesian methods + learning artificial intelligence + predictive models + industrial engineering + data visualization + Bayes methods + D-IFN + learning (artificial intelligence) + Bayesian combination + principal component analysis + subsetsĚuthorProvided Keywords Not Found This paper examines the attribute decomposition approach with simple Bayesian combination for dealing with classification problems that contain high number of attributes and moderate numbers of records. Learn more about bidirectional Unicode characters To review, open the file in an editor that reveals hidden Unicode characters.
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