TY - GEN
T1 - Mining trending high utility itemsets from temporal transaction databases
AU - Hackman, Acquah
AU - Huang, Yu
AU - Tseng, S.
N1 - Publisher Copyright:
© 2018, Springer Nature Switzerland AG.
PY - 2018
Y1 - 2018
N2 - In this paper, we address a novel and important topic in the area of HUI mining, named Trending High Utility Itemset (TrendHUI) mining, with the promise of expanding the applications of HUI mining with the power of trend analytics. We introduce formal definitions for TrendHUI mining and highlighted the importance of the TrendHUI output. Moreover, we develop two algorithms, Two-Phase Trending High Utility Itemset (TP-THUI) miner and Two-Phase Trending High Utility Itemset Guided (TP-THUI-Guided) miner. Both are two-phase algorithms that mine a complete set of TrendHUI. TP-THUI-Guided miner utilizes a remainder utility to calculate the temporal trend of a given itemset to reduce the search space effectively, such that the execution efficiency can be enhanced substantially. Through a series of experiments, using three different datasets, the proposed algorithms prove to be excellent for validity and efficiency. To the best of our knowledge, this is the first work addressing the promising topic on Trending High Utility Itemset mining, which is expected to facilitate numerous applications in data mining fields.
AB - In this paper, we address a novel and important topic in the area of HUI mining, named Trending High Utility Itemset (TrendHUI) mining, with the promise of expanding the applications of HUI mining with the power of trend analytics. We introduce formal definitions for TrendHUI mining and highlighted the importance of the TrendHUI output. Moreover, we develop two algorithms, Two-Phase Trending High Utility Itemset (TP-THUI) miner and Two-Phase Trending High Utility Itemset Guided (TP-THUI-Guided) miner. Both are two-phase algorithms that mine a complete set of TrendHUI. TP-THUI-Guided miner utilizes a remainder utility to calculate the temporal trend of a given itemset to reduce the search space effectively, such that the execution efficiency can be enhanced substantially. Through a series of experiments, using three different datasets, the proposed algorithms prove to be excellent for validity and efficiency. To the best of our knowledge, this is the first work addressing the promising topic on Trending High Utility Itemset mining, which is expected to facilitate numerous applications in data mining fields.
KW - Data mining
KW - High utility itemset
KW - Trend analysis
KW - Utility pattern mining
UR - http://www.scopus.com/inward/record.url?scp=85052829065&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-98812-2_42
DO - 10.1007/978-3-319-98812-2_42
M3 - Conference contribution
AN - SCOPUS:85052829065
SN - 9783319988115
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 461
EP - 470
BT - Database and Expert Systems Applications - 29th International Conference, DEXA 2018, Proceedings
A2 - Pernul, Günther
A2 - Hartmann, Sven
A2 - Ma, Hui
A2 - Hameurlain, Abdelkader
A2 - Wagner, Roland R.
PB - Springer Verlag
T2 - 29th International Conference on Database and Expert Systems Applications, DEXA 2018
Y2 - 3 September 2018 through 6 September 2018
ER -