Download e-book for iPad: Advanced Data Mining and Applications: 7th International by Yong-Bin Kang, Shonali Krishnaswamy (auth.), Jie Tang, Irwin

By Yong-Bin Kang, Shonali Krishnaswamy (auth.), Jie Tang, Irwin King, Ling Chen, Jianyong Wang (eds.)

ISBN-10: 3642258522

ISBN-13: 9783642258527

ISBN-10: 3642258530

ISBN-13: 9783642258534

The two-volume set LNAI 7120 and LNAI 7121 constitutes the refereed complaints of the seventh overseas convention on complicated facts Mining and functions, ADMA 2011, held in Beijing, China, in December 2011. The 35 revised complete papers and 29 brief papers awarded including three keynote speeches have been rigorously reviewed and chosen from 191 submissions. The papers conceal quite a lot of issues offering unique examine findings in information mining, spanning purposes, algorithms, software program and platforms, and utilized disciplines.

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Additional resources for Advanced Data Mining and Applications: 7th International Conference, ADMA 2011, Beijing, China, December 17-19, 2011, Proceedings, Part I

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Precision of maximal frequent itemsets VS. 03 Mimimum Support (d) MUSHROOM Fig. 5. Recall of maximal frequent itemsets VS. Minimum support 39 40 H. Li and N. Zhang to that of the naive method and estMax with a little lower, that is to say, our algorithm mistaken deletes little real results. 6 Conclusions In this paper we considered a problem, which is how to mine maximal frequent itemset over stream using a false negative method, and then proposed our method FNMFIMoDS. In our algorithm, we used Chernoff Bound to prune the infrequent itemsets; plus, we classified the itemsets into categories to prune the un-maximal frequent itemsets, which still can guarantee that we obtain the proper itemsets; thus, our algorithm was able to perform in an incremental manner.

If an itemset X is an actual maximal frequent itemset, and it is covered by possible frequent itemsets, infrequent itemsets or none itemsets, it is called an actual maximal frequent itemset(AMF ). Definition 5(Shifty Un-Maximal Frequent Itemset). If an itemset X is a shifty frequent itemset and covered by shifty frequent itemsets, it is called a shifty un-maximal frequent itemset(SUMF ). Definition 6(Shifty Maximal Frequent Itemset). If an itemset X is a shifty frequent itemset, and it is covered by possible frequent itemsets, infrequent itemsets, or none itemsets, it is called a shifty maximal frequent itemset(SMF ).

3 FNMFIMoDS According to our mining strategies, we propose our algorithm FNMFIMoDS. 1, our algorithm can be separated into three parts. First, we will generate the new itemsets based on the new arriving transactions, with which we update the existed itemsets support. Second, we recompute the new εn , prune the new infrequent itemsets, and reclassify each itemset. Finally, we can output the actual maximal frequent itemsets and the shifty maximal frequent itemsets as the results on demand of users.

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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.)


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