FUIM: Fuzzy Utility Itemset Mining

Because of usefulness and comprehensibility, fuzzy data mining has been extensively studied and is an emerging topic in recent years. Compared with utility-driven itemset mining technologies, fuzzy utility mining not only takes utilities (e.g., profits) into account, but also considers quantities of items in each transaction for discovering high fuzzy utility itemsets (HFUIs). Thus, fuzziness can be regard as a key criterion to select high-utility itemsets, while the exiting algorithms are not efficient enough. In this paper, an efficient one-phase algorithm named Fuzzy-driven Utility Itemset Miner (FUIM) is proposed to find out a complete set of HFUIs effectively. In addition, a novel compact data structure named fuzzy-list keeps the key information from quantitative transaction databases. Using fuzzy-list, FUIM can discover HFUIs from transaction databases efficiently and effectively. Both completeness and correctness of the FUIM algorithm are proved by five theorems. At last, substantial experiments test three terms (runtime cost, memory consumption, and scalability) to confirm that FUIM considerably outperforms the state-of-the-art algorithms.

[1]  F. E..  Principles of Economics , 1890, Nature.

[2]  Philip S. Yu,et al.  UP-Growth: an efficient algorithm for high utility itemset mining , 2010, KDD.

[3]  R. Agarwal.  Fast Algorithms for Mining Association Rules , 1994, VLDB 1994.

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

[5]  Tzung-Pei Hong,et al.  Temporal-Based Fuzzy Utility Mining , 2017, IEEE Access.

[6]  Keith C. C. Chan,et al.  Mining fuzzy association rules , 1997, CIKM '97.

[7]  Philip S. Yu,et al.  A Survey of Utility-Oriented Pattern Mining , 2018, IEEE Transactions on Knowledge and Data Engineering.

[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]  Yeong-Chyi Lee,et al.  Actionable high-coherent-utility fuzzy itemset mining , 2014, Soft Comput..

[10]  Yin-Fu Huang,et al.  A fuzzy approach for mining high utility quantitative itemsets , 2009, 2009 IEEE International Conference on Fuzzy Systems.

[11]  Filip Karlo Dosilovic,et al.  Explainable artificial intelligence: A survey , 2018, 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO).

[12]  Philip S. Yu,et al.  Efficient Algorithms for Mining the Concise and Lossless Representation of High Utility Itemsets , 2015, IEEE Transactions on Knowledge and Data Engineering.

[13]  Tzung-Pei Hong,et al.  Mining association rules from quantitative data , 1999, Intell. Data Anal..

[14]  Tzung-Pei Hong,et al.  Linguistic data mining with fuzzy FP-trees , 2010, Expert Syst. Appl..

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

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

[17]  Hamido Fujita,et al.  A survey of incremental high‐utility itemset mining , 2018, WIREs Data Mining Knowl. Discov..

[18]  Tzung-Pei Hong,et al.  Fuzzy utility mining with upper-bound measure , 2015, Appl. Soft Comput..

[19]  Jian Pei,et al.  Mining frequent patterns without candidate generation , 2000, SIGMOD '00.

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

[21]  Jian Pei,et al.  H-Mine: Fast and space-preserving frequent pattern mining in large databases , 2007 .

[22]  Philip S. Yu,et al.  Mining interesting user behavior patterns in mobile commerce environments , 2012, Applied Intelligence.

[23]  Vincent S. Tseng,et al.  EFIM: a fast and memory efficient algorithm for high-utility itemset mining , 2016, Knowledge and Information Systems.

[24]  Keun Ho Ryu,et al.  High utility itemset mining with techniques for reducing overestimated utilities and pruning candidates , 2014, Expert Syst. Appl..

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

[26]  Tzung-Pei Hong,et al.  A Survey of Fuzzy Data Mining Techniques , 2016 .

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

[28]  Yue-Shi Lee,et al.  Mining High Utility Quantitative Association Rules , 2007, DaWaK.

[29]  Tzung-Pei Hong,et al.  One-Phase Temporal Fuzzy Utility Mining , 2020, 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).

[30]  Vincent S. Tseng,et al.  An efficient algorithm for mining temporal high utility itemsets from data streams , 2008, J. Syst. Softw..

[31]  S. V. K. Kumar,et al.  A Survey: On Association Rule Mining , 2013 .

[32]  Srikumar Krishnamoorthy,et al.  Pruning strategies for mining high utility itemsets , 2015, Expert Syst. Appl..

[33]  Howard J. Hamilton,et al.  A Unified Framework for Utility Based Measures for Mining Itemsets , 2006 .

[34]  Wensheng Gan,et al.  TopHUI: Top-k high-utility itemset mining with negative utility , 2020, 2020 IEEE International Conference on Big Data (Big Data).

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

[36]  Robert LIN,et al.  NOTE ON FUZZY SETS , 2014 .

[37]  Man Hon Wong,et al.  Mining fuzzy association rules in databases , 1998, SGMD.

[38]  Tzung-Pei Hong,et al.  A fast updated algorithm to maintain the discovered high-utility itemsets for transaction modification , 2015, Adv. Eng. Informatics.

[39]  Wensheng Gan,et al.  TOPIC: Top-k High-Utility Itemset Discovering , 2021, ArXiv.

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

[41]  Wensheng Gan,et al.  OSUMI: On-Shelf Utility Mining from Itemset-based Data , 2020, 2020 IEEE International Conference on Big Data (Big Data).