Mining frequent patterns without candidate generation
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Proceedings of the 2007 ACM symposium on Applied computing
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International Journal of Business Intelligence and Data Mining
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CLA'06 Proceedings of the 4th international conference on Concept lattices and their applications
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This paper proposes a method dealing with missing values in the discovery of frequent patterns. Also, it proposes a method that effectively discovers the patterns from examples composed of attributes and their values. The method generates candidate patterns based on the combination of attributes and the combination of attribute values. It evaluates the patterns based on two supports. These supports are calculated based on the number of examples that do not include missing values in attributes composing target items and in all attributes. The proposed method is verified by comparing it with the existing methods dealing with missing values.