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Utility mining emerged to overcome the limitations of frequent itemset miningby considering the utility of an item. Utility of an item is based on user'sinterest or preference. Recently, temporal data mining has become a coretechnical data processing technique to deal with changing data. On-shelfutility mining considers on-shelf time period of item and gets the accurateutility values of itemsets in temporal database. In traditional on-shelfutility mining, profits of all items in databases are considered as positivevalues. However, in real applications, some items may have negative profit.Most of the traditional algorithms are used to handle static database. Inpractical situations, temporal databases are continually appended or updated.High on-shelf utility itemsets needs to be updated. Re-running the temporalmining algorithm every time is ineffective since it neglects previouslydiscovered itemsets. It repeats the work done previously. In this paper, aneffective algorithm is proposed to find high on-shelf utility itemsets withnegative values from the dynamic updated temporal database.

Mining high on-shelf utility itemsets with negative values from dynamic updated database