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.)

The two-volume set LNAI 7120 and LNAI 7121 constitutes the refereed lawsuits 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 disguise quite a lot of themes proposing unique examine findings in facts mining, spanning purposes, algorithms, software program and structures, and utilized disciplines.

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

Example text

In the second image the same maximal clique is not consistently connected to all the objects outside. Each of them is connected to either 2 or 3 objects inside the core, and this is reflected in the similarity majority margins which take low positive and negative values. This is regarded as a weaker cluster core. The core fitness we have defined before tells us how well a maximal clique will serve as a cluster core. If for a given maximal clique we cannot find a better one in its vicinity then we can use this one as a core and expand it with objects that are well connected to it.

9. [37] try to mine all the results with one stream scan: The stream is split into sections, and the frequent itemsets from the previous section will be used as the candidate itemsets of the next section, Chernoff Bound is also used to guarantee the precision. As can be seen, both algorithms aim to obtain the frequent itemsets. We will prove that the Chernoff Bound method can also be effectively used in maximal frequent itemset mining based on our definition. 3 A Naive Method As can be seen from our addressing problem, we can get a naive method to obtain the maximal frequent itemsets over stream using Chernoff Bound.

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. In our EDIU tree, the root node is the itemset includes all distinct items, and all the descend nodes are the subsets. We link each itemset with their subsets, which are sorted by their lexicographical orders.

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