A Fuzzy Algorithm for Mining High Utility Rare Itemsets – FHURI

Classical frequent itemset mining identifies frequent itemsets in transaction databases using only frequency of item occurrences, without considering utility of items. In many real world situations, utility of itemsets are based upon user’s perspective such as cost, profit or revenue and are of significant importance. Utility mining considers using utility factors in data mining tasks. Utility-based descriptive data mining aims at discovering itemsets with high total utility is termed High Utility Itemset mining. High Utility itemsets may contain frequent as well as rare itemsets. Classical utility mining only considers items and their utilities as discrete values. In real world applications, such utilities can be described by fuzzy sets. Thus itemset utility mining with fuzzy modeling allows item utility values to be fuzzy and dynamic over time. In this paper, an algorithm, FHURI (Fuzzy High Utility Rare Itemset Mining) is presented to efficiently and effectively mine very-high (and high) utility rare itemsets from databases, by fuzzification of utility values. FHURI can effectively extract fuzzy high utility rare itemsets by integrating fuzzy logic with high utility rare itemset mining. FHURI algorithm may have practical meaning to real-world marketing strategies. The results are shown using synthetic datasets.