By Qiang Li Zhao, Yan Huang Jiang, Ming Xu (auth.), Longbing Cao, Jiang Zhong, Yong Feng (eds.)
With the ever-growing energy of producing, transmitting, and accumulating large quantities of information, info overloadis nowan approaching problemto mankind. the overpowering call for for info processing isn't just a few higher knowing of knowledge, but in addition a greater utilization of information briskly. facts mining, or wisdom discovery from databases, is proposed to achieve perception into points ofdata and to aid peoplemakeinformed,sensible,and greater judgements. at the present, growing to be recognition has been paid to the research, improvement, and alertness of information mining. hence there's an pressing desire for classy recommendations and toolsthat can deal with new ?elds of knowledge mining, e. g. , spatialdata mining, biomedical facts mining, and mining on high-speed and time-variant facts streams. the information of knowledge mining also needs to be multiplied to new functions. The sixth foreign convention on complicated facts Mining and Appli- tions(ADMA2010)aimedtobringtogethertheexpertsondataminingthrou- out the realm. It supplied a number one overseas discussion board for the dissemination of unique learn leads to complicated facts mining recommendations, functions, 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 overseas specialists in info mining ?elds. a complete variety of 118 papers have been authorized for the convention. among them, sixty three papers have been chosen as ordinary papers and fifty five papers have been chosen as brief papers.
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Additional info for Advanced Data Mining and Applications: 6th International Conference, ADMA 2010, Chongqing, China, November 19-21, 2010, Proceedings, Part II
4 Feature Generation and Selection In this section, we first introduce distribution features in order to extract more detailed information for nominal attributes. Then, we develop an efficient method for selecting pertinent features from both aggregate and distribution features. 1 Distribution Features We observe in Section 3 that simple aggregate attributes may not be able to provide enough information on nominal attributes. Thus, we propose to extend aggregate features to distribution features.
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Data intensive applications are widely existed, such as massive data mining, search engine and high-throughput computing in bioinformatics, etc. Data processing becomes a bottleneck as the scale keeps bombing. However, the cost of processing the large scale dataset increases dramatically in traditional relational database, because traditional technology inclines to adopt high performance computer. The boost of cloud computing brings a new solution for data processing due to the characteristics of easy scalability, robustness, large scale storage and high performance.
Advanced Data Mining and Applications: 6th International Conference, ADMA 2010, Chongqing, China, November 19-21, 2010, Proceedings, Part II by Qiang Li Zhao, Yan Huang Jiang, Ming Xu (auth.), Longbing Cao, Jiang Zhong, Yong Feng (eds.)