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 foreign convention on complex facts Mining and purposes, 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 conscientiously reviewed and chosen from 191 submissions. The papers conceal a variety of themes featuring unique examine findings in information mining, spanning functions, 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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The data characteristics are summarized in Tab. 1. The estMax algorithm in [29] is a state-of-the-art method for mining maximal frequent itemsets over stream; thus, we use it as the evaluated method for comparison. 01; also, we employ our presented naive algorithm as another evaluated method. 1, which denotes a 10 percent probability for mistaken deleting the actual maximal frequent itemsets. A False Negative Maximal Frequent Itemset Mining Algorithm over Stream 37 Table 1. Data Characteristics DataSet nr.

The basic maximal frequent itemset mining method is based on the a priori property of the itemset. The implementations were separated into two types: One type is an improvement of the a priori mining method, a breadth first search[32], with utilizing data pruning, nevertheless, the candidate results are huge when an itemset is large; a further optimization was the bottom-up method, which counted the weight from the largest itemset to avoid superset checking, also, the efficiency was low when the threshold was small.

Let us consider in Figure 1 a set of objects {a, b, c, d} that are described by four attributes, one cardinal and three ordinal. We may notice that objects a, b and c are quite small, while d is significantly larger. On the second attribute a and b, as well as c and d have the same texture. On the color attribute we notice some objects are dark, and some are light or we could consider each color level to be different. This can be perceived differently by anyone who looks at these objects. On the last attribute, we have the shapes of each object, and we could consider that object a is different from b but similar to the rest, object b is similar to both a and c but different from d and c is also different from d.

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