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Download Advanced Data Mining and Applications: 6th International by Qiang Li Zhao, Yan Huang Jiang, Ming Xu (auth.), Longbing PDF

By Qiang Li Zhao, Yan Huang Jiang, Ming Xu (auth.), Longbing Cao, Jiang Zhong, Yong Feng (eds.)

With the ever-growing strength of producing, transmitting, and accumulating large quantities of information, details overloadis nowan impending problemto mankind. the overpowering call for for info processing is not only a couple of greater knowing of knowledge, but in addition a greater utilization of knowledge quickly. facts mining, or wisdom discovery from databases, is proposed to achieve perception into facets ofdata and to aid peoplemakeinformed,sensible,and greater judgements. at the present, growing to be realization has been paid to the research, improvement, and alertness of information mining. consequently there's an pressing desire for stylish innovations and toolsthat can deal with new ?elds of knowledge mining, e. g. , spatialdata mining, biomedical info mining, and mining on high-speed and time-variant information streams. the information of information mining must also be increased to new purposes. The sixth overseas convention on complex facts Mining and Appli- tions(ADMA2010)aimedtobringtogethertheexpertsondataminingthrou- out the realm. It supplied a number one foreign discussion board for the dissemination of unique examine leads to complicated info mining recommendations, purposes, al- rithms, software program and structures, and di?erent utilized disciplines. The convention attracted 361 on-line submissions from 34 di?erent nations and parts. All complete papers have been peer reviewed by way of at the very least 3 participants of this system Comm- tee composed of foreign specialists in info mining ?elds. a complete variety of 118 papers have been approved for the convention. among them, sixty three papers have been chosen as normal papers and fifty five papers have been chosen as brief papers.

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Extra info for Advanced Data Mining and Applications: 6th International Conference, ADMA 2010, Chongqing, China, November 19-21, 2010, Proceedings, Part II

Example text

Select feature Af in C of the maximum similarity 7. if sim(Dc, Af ) <= min_sim, then break 8. remove Af from C 9. if exist Af’ in P that sim(Af, Af’ ) >= max_select, then continue 10. add Af into P 11. for each target tuple ti 12. if cover(ti) <= min_cov, then increase the weight of ti in the weight matrix W 13. if W is updated, then update the similarity of features in C 14. for each inactive Rj that can be appended to a path from Rt to Ri which contains A 15. activate Rj 16. end return P 28 M.

3) Example 5 (Similarity matrix). Suppose in our running example, the target relation LOAN contains only three target tuples whose distribution feature vectors on Type are shown in Fig. 4. 67⎤ . 67 1 ⎥⎦ Using the similarity matrices, we can measure the feature similarity. Particularly, we are interested in similarity between a feature and the class distribution feature, which is a kind of distribution feature where the vector contains one 1and 0 for others. Definition 5 (Feature similarity). The similarity between a feature Af, no matter an aggregate one or a distribution one, and the class distribution feature Dc is defined as sim( Dc , A f ) = V Dc ⋅ V Af V Dc ⋅ V Af .

29–43. Lake George, New York (2003) 9. com 10. hk/alphaminer/ 11. : Interpreting the data: Parallel analysis with Sawzall. Scientific Programming Journal 13(4) (2005) 12. : Yahoo! Research Pig Latin: A Not-So-Foreign Language for Data Processing. In: Proceedings of the 2008 ACM SIGMOD International Conference on Management of data (2008) 13. com/research/sv/DryadLINQ/ 14. : Dryad: Distributed data-parallel programs from sequential building blocks. In: European Conference on Computer Systems (EuroSys), Lisbon, Portugal, pp.

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