Mining Recent High-Utility Patterns from Temporal Databases with Time-Sensitive Constraint

Useful knowledge embedded in a database is likely to be changed over time. Identifying recent changes and up-to-date information in temporal databases can provide valuable information. In this paper, we address this issue by introducing a novel framework, named recent high-utility pattern mining from temporal databases with time-sensitive constraint (RHUPM) to mine the desired patterns based on user-specified minimum recency and minimum utility thresholds. An efficient tree-based algorithm called RUP, the global and conditional downward closure (GDC and CDC) properties in the recency-utility (RU)-tree are proposed. Moreover, the vertical compact recency-utility (RU)-list structure is adopted to store necessary information for later mining process. The developed RUP algorithm can recursively discover recent HUPs; the computational cost and memory usage can be greatly reduced without candidate generation. Several pruning strategies are also designed to speed up the computation and reduce the search space for mining the required information.

[1]  Ron Rymon,et al.  Search through Systematic Set Enumeration , 1992, KR.

[2]  Ying Liu,et al.  A Two-Phase Algorithm for Fast Discovery of High Utility Itemsets , 2005, PAKDD.

[3]  Cory J. Butz,et al.  A Foundational Approach to Mining Itemset Utilities from Databases , 2004, SDM.

[4]  Tzung-Pei Hong,et al.  Mining high-utility itemsets with various discount strategies , 2015, 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA).

[5]  Qiang Yang,et al.  Mining high utility itemsets , 2003, Third IEEE International Conference on Data Mining.

[6]  Tzung-Pei Hong,et al.  Mining High-Utility Itemsets with Multiple Minimum Utility Thresholds , 2015, C3S2E.

[7]  Tzung-Pei Hong,et al.  Efficient algorithms for mining up-to-date high-utility patterns , 2015, Adv. Eng. Informatics.

[8]  Young-Koo Lee,et al.  Efficient Tree Structures for High Utility Pattern Mining in Incremental Databases , 2009, IEEE Transactions on Knowledge and Data Engineering.

[9]  Vincent S. Tseng,et al.  FHM: Faster High-Utility Itemset Mining Using Estimated Utility Co-occurrence Pruning , 2014, ISMIS.

[10]  Ramakrishnan Srikant,et al.  Fast Algorithms for Mining Association Rules in Large Databases , 1994, VLDB.

[11]  Tzung-Pei Hong,et al.  Discovery of high utility itemsets from on-shelf time periods of products , 2011, Expert Syst. Appl..

[12]  Jian Pei,et al.  Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach , 2006, Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06).

[13]  Tomasz Imielinski,et al.  Database Mining: A Performance Perspective , 1993, IEEE Trans. Knowl. Data Eng..

[14]  Philip S. Yu,et al.  Efficient Algorithms for Mining High Utility Itemsets from Transactional Databases , 2013, IEEE Transactions on Knowledge and Data Engineering.

[15]  Jerry Chun-Wei Lin,et al.  Efficient Incremental High Utility Itemset Mining , 2015, ASE BD&SI.

[16]  Rupali A. Mahajan,et al.  Survey on Mining High Utility Itemset from Transactional Database , 2013 .

[17]  Mengchi Liu,et al.  Mining high utility itemsets without candidate generation , 2012, CIKM.

[18]  Philip S. Yu,et al.  Efficient Algorithms for Mining Top-K High Utility Itemsets , 2016, IEEE Transactions on Knowledge and Data Engineering.