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 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.
Read Online or Download Advanced Data Mining and Applications: 7th International Conference, ADMA 2011, Beijing, China, December 17-19, 2011, Proceedings, Part I PDF
Best mining books
This publication covers the basic ideas of information mining, to illustrate the possibility of accumulating huge units of knowledge, and interpreting those information units to achieve valuable enterprise realizing. The ebook is prepared in 3 components. half I introduces recommendations. half II describes and demonstrates easy info mining algorithms.
The booklet reports equipment for the numerical and statistical research of astronomical datasets with specific emphasis at the very huge databases that come up from either latest and approaching tasks, in addition to present large-scale computing device simulation experiences. best specialists supply overviews of state of the art tools acceptable within the zone of astronomical info mining.
This ebook describes the seismic tools utilized in geophys ical exploration for oil and gasoline in a finished, non rigorous, mathematical demeanour. i've got used it and its predecessors as a handbook for brief classes in seismic equipment, and it's been widely revised again and again to incorporate the most recent advances in our really comment capable technological know-how.
- Advanced Data Mining and Applications: 7th International Conference, ADMA 2011, Beijing, China, December 17-19, 2011, Proceedings, Part II
- Petroleum Rock Mechanics. Drilling Operations and Well Design
- Handbook of Flotation Reagents: Chemistry, Theory and Practice: Volume 1: Flotation of Sulfide Ores
- Handbook for Methane Control in Mining
Additional resources for Advanced Data Mining and Applications: 7th International Conference, ADMA 2011, Beijing, China, December 17-19, 2011, Proceedings, Part I
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.
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.)