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