Advanced Data Mining and Applications: 5th International by Edward Y. Chang (auth.), Ronghuai Huang, Qiang Yang, Jian

By Edward Y. Chang (auth.), Ronghuai Huang, Qiang Yang, Jian Pei, João Gama, Xiaofeng Meng, Xue Li (eds.)

This quantity comprises the court cases of the overseas convention on complex information Mining and purposes (ADMA 2009), held in Beijing, China, in the course of August 17–19, 2009. we're happy to have a really robust software. recognition into the convention complaints used to be tremendous aggressive. From the 322 submissions from 27 international locations and areas, this system Committee chosen 34 complete papers and forty seven brief papers for presentation on the convention and inclusion within the complaints. The c- tributed papers conceal a variety of information mining issues and a various spectrum of fascinating purposes. this system Committee labored very demanding to pick those papers via a rigorous evaluate method and wide dialogue, and eventually c- posed a various and intriguing application for ADMA 2009. a big function of the most software was once the really impressive keynote spe- ers application. Edward Y. Chang, Director of study, Google China, gave a conversation titled "Confucius and 'Its' clever Disciples". Being correct within the leading edge of knowledge mining functions to the world's greatest wisdom and information base, the internet, Dr. Chang - scribed how Google's wisdom seek product aid to enhance the scalability of laptop studying for Web-scale purposes. Charles X. Ling, a professional researcher in facts mining from the collage of Western Ontario, Canada, referred to his in- vative purposes of information mining and synthetic intelligence to talented baby education.

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Extra resources for Advanced Data Mining and Applications: 5th International Conference, ADMA 2009, Beijing, China, August 17-19, 2009. Proceedings

Sample text

Next, we focus on positive similarities only. Indeed, the latter are related to pairs of vectors whose cosine index is positive which indicates that they are rather similar. Thus, let S+ be the set of pairs of objects having positive similarities: S+ = {(oi , oi ) ∈ O2 : Sii ≥ 0}. Then, we compute the central tendency measures related to the clustering criteria, on the basis of pairs belonging to S+ . More concretely, below are the clustering functions that we propose to define: B + (S, X) = Si+.

Table 2 shows that alignment between the two sets of clusters is 100% when k = (5, 10, 15, 20, 40) for both domains, News Briefs and Features. However, as the number of clusters increases, there are more clusters that are unaligned between the mappings. This is probably due to the fact that Bulgarian documents have a greater number of distinct terms. As the Bulgarian language has more word forms to express the same concepts as English phrases, this may affect the computation of weights for the terms during the clustering process.

Hopefully, in our context, we can reduce the complexity cost of these quantities. Let us recall that, we are given the feature matrix T of size (N × P ) as input. Furthermore, let us assume that the space dimension is much lower6 than the number of objects, P << N . Then, since S = T · T , we can use the linearity properties of the dot products in order to quickly compute the contributions (14) and (15) by using prototypes. First, one can observe that: oi , oi = oi , hl Sii = i :oi ∈ul where hl = i :oi ∈ul oi .

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