Abstract
The discovery of frequent patterns is one of the most important issues in the data mining area. A major difficulty concerning frequent patterns is huge amount of discovered patterns. The problem can be solved or at least significantly alleviated by applying concise representations of frequent patterns. A number of most concise representations use generalized disjunctive rules for reasoning about patterns. Recently, the representation based on essential patterns has been introduced, but was not confronted with the representations using generalized disjunctive rules. In this paper, we 1) prove that essential patterns with at least two elements can be defined equivalently in terms of generalized disjunctive rules of a particular subtype and that singleton patterns are essential if their supports do not equal 0, 2) identify the relationship between compressed disjunction-free patterns and essential ones, 3) propose new lossless representation E-CDFR of frequent patterns that is primarily based on compressed disjunction-free patterns and uses generalized disjunctive rules to reason about other patterns, 4) prove that the new representation is never less concise than the representation based on essential patterns.
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Kryszkiewicz, M. (2009). Compressed Disjunction-Free Pattern Representation versus Essential Pattern Representation. In: Corchado, E., Yin, H. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2009. IDEAL 2009. Lecture Notes in Computer Science, vol 5788. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04394-9_43
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DOI: https://doi.org/10.1007/978-3-642-04394-9_43
Publisher Name: Springer, Berlin, Heidelberg
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