TIFIM: Tree based Incremental Frequent Itemset Mining over Streaming Data
作者： V.sidda Reddy , Dr T.V. Rao , Dr A. Govardhan
关键词: Property (programming) 、 Data mining 、 Tree (data structure) 、 Node (networking) 、 Space (commercial competition) 、 Table (database) 、 Data stream mining 、 Computer science 、 Scalability 、 Relaxation (approximation)
摘要: Data Stream Mining algorithms performs under constraints called space used and time taken, which is due to the streaming property. The relaxation in these inversely proportional speed of data. Since caching mining streaming-data sensitive, here this paper a scalable, memory efficient frequent itemset model devised. proposed an incremental approach that builds single level multi node trees bushes from each window data; henceforth we refer algorithm as Tree (bush) based Incremental Frequent Itemset (TIFIM) over data streams. /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin:0in; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times Roman"; mso-bidi-theme-font:minor-bidi;}
cirworld.com 参考文章(10)
Chowdhury Farhan Ahmed, Syed Khairuzzaman Tanbeer, Byeong-Soo Jeong, Efficient Mining of High Utility Patterns over Data Streams with a Sliding Window Method Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing 2010. pp. 99- 113 ,(2010) , 10.1007/978-3-642-13265-0_8
Moses Charikar, Kevin Chen, Martin Farach-Colton, Finding Frequent Items in Data Streams international colloquium on automata languages and programming. ,vol. 312, pp. 693- 703 ,(2002) , 10.1016/S0304-3975(03)00400-6
Fan Guidan, Yin Shaohong, A Frequent Itemsets Mining Algorithm Based on Matrix in Sliding Window over Data Streams international conference on intelligent systems design and engineering applications. pp. 66- 69 ,(2013) , 10.1109/ISDEA.2012.23
Pauray S.M. Tsai, Mining frequent itemsets in data streams using the weighted sliding window model Expert Systems With Applications. ,vol. 36, pp. 11617- 11625 ,(2009) , 10.1016/J.ESWA.2009.03.025
Jing Guo, Peng Zhang, Jianlong Tan, Li Guo, Mining frequent patterns across multiple data streams Proceedings of the 20th ACM international conference on Information and knowledge management - CIKM '11. pp. 2325- 2328 ,(2011) , 10.1145/2063576.2063957
Gurmeet Singh Manku, Rajeev Motwani, Approximate frequency counts over data streams Proceedings of the VLDB Endowment. ,vol. 5, pp. 1699- 1699 ,(2012) , 10.14778/2367502.2367508
Syed Khairuzzaman Tanbeer, Chowdhury Farhan Ahmed, Byeong-Soo Jeong, Young-Koo Lee, None, Efficient frequent pattern mining over data streams Proceeding of the 17th ACM conference on Information and knowledge mining - CIKM '08. pp. 1447- 1448 ,(2008) , 10.1145/1458082.1458326
Ramakrishnan Srikant, John Shafer, Toni Bollinger, Andreas Arning, Rakesh Agrawal, Manish Mehta, The Quest Data mining System knowledge discovery and data mining. pp. 244- 249 ,(1996)
Mohamed Medhat Gaber, Arkady Zaslavsky, Shonali Krishnaswamy, Mining data streams: a review international conference on management of data. ,vol. 34, pp. 18- 26 ,(2005) , 10.1145/1083784.1083789
Nan Jiang, Le Gruenwald, CFI-Stream: mining closed frequent itemsets in data streams knowledge discovery and data mining. pp. 592- 597 ,(2006) , 10.1145/1150402.1150473