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 complaints of the seventh overseas convention on complex 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 rigorously reviewed and chosen from 191 submissions. The papers disguise quite a lot of subject matters featuring unique learn findings in facts mining, spanning functions, algorithms, software program and structures, and utilized disciplines.
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Extra info for Advanced Data Mining and Applications: 7th International Conference, ADMA 2011, Beijing, China, December 17-19, 2011, Proceedings, Part I
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. In: Proc. VLDB (2004) 37. : Finding frequent itemsets in high-speed data streams. In: Proc. fr Abstract. Several researchers have illustrated that data privacy is an important and inevitable constraint when dealing with distributed knowledge discovery.
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. 3, our algorithm reaches to a much lower memory cost than the naive method and estMax.
Note again that this step should be carried over only a small sample of the original dataset. The second step is straight-forward and derives from the deﬁnitions in the above section. In the third step, we may use two resolution strategies: exact enumeration of all the maximal cliques and selection of the ﬁttest ones as potential cluster cores, or a population-based metaheuristic approach. For the exact approach we use the Bron-Kerbosch algorithm , with the pivot point improvement from Koch .
Advanced Data Mining and Applications: 7th International Conference, ADMA 2011, Beijing, China, December 17-19, 2011, Proceedings, Part I by Yong-Bin Kang, Shonali Krishnaswamy (auth.), Jie Tang, Irwin King, Ling Chen, Jianyong Wang (eds.)