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

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 court cases of the seventh overseas convention on complicated info Mining and purposes, ADMA 2011, held in Beijing, China, in December 2011. The 35 revised complete papers and 29 brief papers provided including three keynote speeches have been conscientiously reviewed and chosen from 191 submissions. The papers conceal quite a lot of subject matters providing unique learn findings in information mining, spanning purposes, algorithms, software program and platforms, and utilized disciplines.

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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

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Consequently, if they are actual un-maximal frequent itemsets, we use the the support of their covering itemset as their supports; if they are actual inter-maximal frequent itemsets, we use λ + εn as their supports. , the actual frequent itemsets will become shifty frequent at most. Indexing Data. To speed up itemsets comparison and results output, we will build the index on the 3-tuples in F1 . Since our aim is to reduce the memory cost, the index will be as simple as possible. Consequently, we use an extended lexicographical ordered direct update tree(EDIU tree) rather than a traditional prefix tree or an enumeration tree.

Zhang Definition 4(Actual Maximal Frequent Itemset). 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 ).

Three kinds of frequent itemset mining approaches over static databases have been proposed: reading-based[3], writing-based[15], and pointerbased[18]. [14] presented a comprehensive survey of frequent itemset mining and discussed research directions. Many methods focusing on frequent itemset mining over a stream have been proposed. [13] proposed FP-Stream to mine frequent itemsets, which was efficient when the average transaction length was small; [22] used lossy counting to mine This research is supported by the National Science Foundation of China(61100112), and Program for Innovation Research in Central University of Finance and Economics.

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