Generalized itemset mining is an established data mining technique that focuses on discovering high-level correlations among large databases. By exploiting a taxonomy built over the data items, items are aggregated into higher level concepts and, thus, data correlations at different abstraction levels can be discovered. However, since a large number of patterns can be extracted, the result of t...

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...

Large data volumes have been collected by healthcare organizations at an unprecedented rate. Today both physicians and healthcare system managers are very interested in extracting value from such data. Nevertheless, the increasing data complexity and heterogeneity prompts the need for new efficient and effective data mining approaches to analyzing large patient datasets. Generalized association...

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