About: High utility itemset mining consists of identifying all the sets of items that appear together and yield a high profit in a customer transaction database. Recently, this problem was extended to discover trending high utility itemsets (itemsets that yield an increasing or decreasing profit over time). However, an important limitation of that problem is that it is assumed that trends remain stable over time. But in real-life, trends may change in different time intervals due to specific events. To identify time intervals where itemsets have increasing/decreasing trends in terms of utility, this paper proposes the problem of mining Locally Trending High Utility Itemsets (LTHUIs) and their Trending High Utility Periods (THUPs). Properties of the problem are studied and an efficient algorithm named LTHUI-Miner is proposed to enumerate all the LTHUIs and their THUPs. An experimental evaluation shows that the algorithm is efficient and can discover insightful patterns not found by previous algorithms.   Goto Sponge  NotDistinct  Permalink

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  • High utility itemset mining consists of identifying all the sets of items that appear together and yield a high profit in a customer transaction database. Recently, this problem was extended to discover trending high utility itemsets (itemsets that yield an increasing or decreasing profit over time). However, an important limitation of that problem is that it is assumed that trends remain stable over time. But in real-life, trends may change in different time intervals due to specific events. To identify time intervals where itemsets have increasing/decreasing trends in terms of utility, this paper proposes the problem of mining Locally Trending High Utility Itemsets (LTHUIs) and their Trending High Utility Periods (THUPs). Properties of the problem are studied and an efficient algorithm named LTHUI-Miner is proposed to enumerate all the LTHUIs and their THUPs. An experimental evaluation shows that the algorithm is efficient and can discover insightful patterns not found by previous algorithms.
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  • SI base quantities
  • Order theory
  • Concepts in the philosophy of mind
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