By Yong-Bin Kang, Shonali Krishnaswamy (auth.), Jie Tang, Irwin King, Ling Chen, Jianyong Wang (eds.)
The two-volume set LNAI 7120 and LNAI 7121 constitutes the refereed lawsuits of the seventh foreign convention on complicated info Mining and functions, ADMA 2011, held in Beijing, China, in December 2011. The 35 revised complete papers and 29 brief papers offered including three keynote speeches have been conscientiously reviewed and chosen from 191 submissions. The papers disguise quite a lot of subject matters providing unique learn findings in information mining, spanning purposes, algorithms, software program and structures, and utilized disciplines.
Read Online or Download Advanced Data Mining and Applications: 7th International Conference, ADMA 2011, Beijing, China, December 17-19, 2011, Proceedings, Part I PDF
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Additional info for Advanced Data Mining and Applications: 7th International Conference, ADMA 2011, Beijing, China, December 17-19, 2011, Proceedings, Part I
In: Proc. RIDE (2005) 31. : Eﬃciently Mining Maximal Frequent Itemsets. In: Proc. ICDM (2001) 32. : Eﬃciently Mining Long Patterns from Databases. In: Proc. SIGMOD (1998) 33. : Depth First Generation of Long Patterns. In: Proc. SIGKDD (2000) 34. : MAFIA: A Maximal Frequent Itemsets Algorithm for Transactional Databases. In: Proc. ICDE (2001) 35. : The Complexity of Mining Maximal Frequent Itemsets and Maximal Frequent Patterns. In: Proc. SIGKDD (2004) 36. : False positive or false negative: Mining frequent itemsets from high speed transactional data streams.
Max. nr. trans. trans. trans. trans. of corr. 2, when the minimum support decreases, the running time cost of these three algorithms increase over all datasets. Our algorithm is the best in runtime cost, the reason is that our algorithm prunes some useless computing with classifying the itemsets, as well employs an EDIU tree to index itemsets. The naive method is the worst in runtime cost, since we almost use no optimizations for it; nevertheless, the false negative technique reduces the comparing count, thus the running time decreases, and the computing eﬃciency can reach to that of estMax on the MUSHROOM dataset and T40I10D100K dataset.
Third, when we prune some itemsets, which is a frequent operation especially in our algorithm, the pruning eﬃciency is high since most itemsets can be deleted in a cascaded matter. 0GHz PC with 2GB RAM. We used 2 synthetic datasets and 2 real-life datasets, which are well-known benchmarks for frequent itemset mining. The T10I4D100K and T40I10D100K 36 H. Li and N. Zhang Algorithm 1. FNMFIMoDS Function Require: F1 : Existing 3-tuples of frequent itemsets and potential frequent itemsets; n1 : The number of arrived transactions; F2 : New 3-tuples of frequent itemsets and potential frequent itemsets; n2 : The number of new arriving transactions; λ: Minimum support; ϕ: Probability; ε: False negative parameter; ; 1: n2 = 2+2ln(2/ϕ) λ 2: for each n2 new arriving transactions do 3: obtain F2 ; 4: n1 = n1 + n2 ; 5: F1 = F1 ∪ F2 ; ; 6: ε = 2sln(2/ϕ) n1 7: prune the new infrequent itemsets from F1 ; 8: for each 3-tuple I in F1 do 9: if I.