Mining summarization of high utility itemsets

Mining interesting itemsets from transaction databases has attracted a lot of research interests for decades. In recent years, high utility itemset (HUI) has emerged as a hot topic in this field. In real applications, the bottleneck of HUI mining is not at the efficiency but at the interpretability, due to the huge number of itemsets generated by the mining process. Because the downward closure property of itemsets no longer holds for HUIs, the compression or summarization methods for frequent itemsets are not available. With this in mind, considering coverage and diversity, we introduce a novel well-founded approach, called SUIT-miner, for succinctly summarizing HUIs with a small collection of itemsets. First, we define the condition under which an itemset can cover another itemset. Then, a greedy algorithm is presented to find the least itemsets to cover all of HUIs, in order to ensure diversity. For enhancing the efficiency, the greedy algorithm employs some pruning strategies. To evaluate the performance of SUIT-miner, we conduct extensive experiments on real datasets. The experimental results show that SUIT-miner is effective and efficient.

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

[2]  Dimitrios Gunopulos,et al.  Data mining, hypergraph transversals, and machine learning (extended abstract) , 1997, PODS.

[3]  Young-Koo Lee,et al.  HUC-Prune: an efficient candidate pruning technique to mine high utility patterns , 2011, Applied Intelligence.

[4]  Nicolas Pasquier,et al.  Discovering Frequent Closed Itemsets for Association Rules , 1999, ICDT.

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

[6]  Howard J. Hamilton,et al.  Extracting Share Frequent Itemsets with Infrequent Subsets , 2003, Data Mining and Knowledge Discovery.

[7]  Toon Calders,et al.  Mining All Non-derivable Frequent Itemsets , 2002, PKDD.

[8]  Jiawei Han,et al.  Mining Compressed Frequent-Pattern Sets , 2005, VLDB.

[9]  Chin-Chen Chang,et al.  Isolated items discarding strategy for discovering high utility itemsets , 2008, Data Knowl. Eng..

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

[11]  Dimitrios Gunopulos,et al.  Data mining, hypergraph transversals, and machine learning (extended abstract) , 1997, PODS '97.

[12]  A. Choudhary,et al.  A fast high utility itemsets mining algorithm , 2005, UBDM '05.

[13]  Jieh-Shan Yeh,et al.  Efficient algorithms for incremental utility mining , 2008, ICUIMC '08.

[14]  Jiawei Han,et al.  Summarizing itemset patterns: a profile-based approach , 2005, KDD '05.

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

[16]  Chin-Chen Chang,et al.  Direct Candidates Generation: A Novel Algorithm for Discovering Complete Share-Frequent Itemsets , 2005, FSKD.

[17]  Huy Nguyen,et al.  Parallel Method for Mining High Utility Itemsets from Vertically Partitioned Distributed Databases , 2009, KES.

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

[19]  Unil Yun,et al.  A fast perturbation algorithm using tree structure for privacy preserving utility mining , 2015, Expert Syst. Appl..

[20]  Anthony K. H. Tung,et al.  Fault-Tolerant Frequent Pattern Mining: Problems and Challenges , 2001, DMKD.

[21]  Ming-Yen Lin,et al.  High utility pattern mining using the maximal itemset property and lexicographic tree structures , 2012, Inf. Sci..

[22]  Keun Ho Ryu,et al.  Efficient frequent pattern mining based on Linear Prefix tree , 2014, Knowl. Based Syst..

[23]  Vincent S. Tseng,et al.  Efficient Mining of Temporal High Utility Itemsets from Data streams , 2006 .

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

[25]  Tzung-Pei Hong,et al.  An effective tree structure for mining high utility itemsets , 2011, Expert Syst. Appl..

[26]  Philip S. Yu,et al.  Mining High Utility Mobile Sequential Patterns in Mobile Commerce Environments , 2011, DASFAA.

[27]  Zhi-Hong Deng,et al.  Fast mining Top-Rank-k frequent patterns by using Node-lists , 2014, Expert Syst. Appl..

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

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

[30]  Chinchen Chang,et al.  EFFICIENT ALGORITHMS FOR MINING SHARE-FREQUENT ITEMSETS , 2005 .

[31]  Zhonghui Wang,et al.  A new algorithm for fast mining frequent itemsets using N-lists , 2012, Science China Information Sciences.

[32]  Vipin Kumar,et al.  Support envelopes: a technique for exploring the structure of association patterns , 2004, KDD.

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

[34]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[35]  Chin-Chen Chang,et al.  A Fast Algorithm for Mining Share-Frequent Itemsets , 2005, APWeb.

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

[37]  Srinivasan Parthasarathy,et al.  Summarizing itemset patterns using probabilistic models , 2006, KDD '06.

[38]  Raj P. Gopalan,et al.  Efficient Mining of High Utility Itemsets from Large Datasets , 2008, PAKDD.

[39]  Benjamin C. M. Fung,et al.  Direct Discovery of High Utility Itemsets without Candidate Generation , 2012, 2012 IEEE 12th International Conference on Data Mining.

[40]  Cheng Yang,et al.  Efficient discovery of error-tolerant frequent itemsets in high dimensions , 2001, KDD '01.

[41]  Philip S. Yu,et al.  Online mining of temporal maximal utility itemsets from data streams , 2010, SAC '10.

[42]  Zhi-Hong Deng,et al.  Fast mining frequent itemsets using Nodesets , 2014, Expert Syst. Appl..

[43]  Yang Xiang,et al.  Effective and efficient itemset pattern summarization: regression-based approaches , 2008, KDD.

[44]  Suh-Yin Lee,et al.  Fast and Memory Efficient Mining of High Utility Itemsets in Data Streams , 2008, 2008 Eighth IEEE International Conference on Data Mining.