Misleading Generalized Itemset discovery

Frequent generalized itemset mining is a data mining technique utilized to discover a high-level view of interesting knowledge hidden in the analyzed data. By exploiting a taxonomy, patterns are usually extracted at any level of abstraction. However, some misleading high-level patterns could be included in the mined set. This paper proposes a novel generalized itemset type, namely theMisleading Generalized Itemset (MGI). Each MGI, denoted as X ⊲ E , represents a frequent generalized itemset X and its set E of low-level frequent descendants for which the correlation type is in contrast to the one of X. To allow experts to analyze the misleading high-level data correlations separately and exploit such knowledge by making different decisions, MGIs are extracted only if the low-level descendant itemsets that represent contrasting correlations cover almost the same portion of data as the high-level (misleading) ancestor. An algorithm to mine MGIs at the top of traditional generalized itemsets is also proposed. ∗Corresponding author. Tel.: +39 011 090 7084. Fax: +39 011 090 7099. Email addresses: [email protected] (Luca Cagliero), [email protected] (Tania Cerquitelli), [email protected] (Paolo Garza), [email protected] (Luigi Grimaudo) Preprint submitted to The experiments performed on both real and synthetic datasets demonstrate the effectiveness and efficiency of the proposed approach.