Mining

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

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 amassing large quantities of knowledge, info overloadis nowan approaching problemto mankind. the overpowering call for for info processing is not only a couple of higher figuring out of knowledge, but in addition a greater utilization of information rapidly. info mining, or wisdom discovery from databases, is proposed to realize perception into elements ofdata and to aid peoplemakeinformed,sensible,and larger judgements. at the moment, becoming recognition has been paid to the learn, improvement, and alertness of information mining. hence there's an pressing desire for stylish thoughts and toolsthat can deal with new ?elds of information mining, e. g. , spatialdata mining, biomedical facts mining, and mining on high-speed and time-variant facts streams. the data of knowledge mining must also be improved to new purposes. The sixth foreign convention on complex info 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 complex info mining thoughts, purposes, al- rithms, software program and structures, and di?erent utilized disciplines. The convention attracted 361 on-line submissions from 34 di?erent international locations and components. All complete papers have been peer reviewed through no less than 3 participants of this system Comm- tee composed of foreign specialists in information mining ?elds. a complete variety of 118 papers have been permitted for the convention. among them, sixty three papers have been chosen as usual papers and fifty five papers have been chosen as brief papers.

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

Example text

Upon them, a workflow mechanism is devised to orchestrate them. The reason why we design a workflow system is that we discover that single MapReduce cannot be too complex because of the restriction of its program model. Almost every meaningful result generated from the raw dataset needs a series of operations. We provide a user-friendly GUI to help create workflow of operations through the GUI, user can create data processing workflow that satisfies their complicate requirements. The more MapReduce jobs in a workflow, the more read and write of intermediate results, hence the more execution time it needs.

Support Vector Machines (SVM) are believed to be as powerful as Artificial Neural Networks (ANN) in modeling complex problems while avoiding some of the drawbacks of the latter such as local minimæ or reliance on architecture. However, a question that remains to be answered is whether SVM users may expect improvements in the interpretability of their models, namely by using rule extraction methods already available to ANN users. This study successfully applies the Orthogonal Search-based Rule Extraction algorithm (OSRE) to Support Vector Machines.

ADMA 2010, Part II, LNCS 6441, pp. 13–20, 2010. © Springer-Verlag Berlin Heidelberg 2010 14 Z. Zhong et al. operation indicates a MapReduce job. Upon them, a workflow mechanism is devised to orchestrate them. The reason why we design a workflow system is that we discover that single MapReduce cannot be too complex because of the restriction of its program model. Almost every meaningful result generated from the raw dataset needs a series of operations. We provide a user-friendly GUI to help create workflow of operations through the GUI, user can create data processing workflow that satisfies their complicate requirements.

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