By Heng Chen, Yi Jin, Yan Zhao, Yongjuan Zhang (auth.), Petra Perner (eds.)
This e-book constitutes the refereed court cases of the thirteenth commercial convention on info Mining, ICDM 2013, held in ny, big apple, in July 2013. The 22 revised complete papers provided have been conscientiously reviewed and chosen from 112 submissions. the subjects diversity from theoretical facets of information mining to functions of knowledge mining, resembling in multimedia information, in advertising and marketing, finance and telecommunication, in drugs and agriculture, and in technique regulate, and society.
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Additional resources for Advances in Data Mining. Applications and Theoretical Aspects: 13th Industrial Conference, ICDM 2013, New York, NY, USA, July 16-21, 2013. Proceedings
Some reasons for the growing amount of collected data are (a) data are usually permanently stored for further analysis, and (b) the emergence of novel scientiﬁc instruments with increase of quality and/or resolution of sensor devices. For example in the breath research domain the number of detected (protonated) compounds increased from about 200 to 2000 compounds by changing from PTR-MS (Proton Transfer Reaction Mass Spectrometry) to PTR-TOF-MS (Proton Transfer Reaction Time of Flight Mass Spectrometry) .
The common activities use the local or remote Towards a High Productivity Automatic Analysis Framework 29 machines for calculations. The AAF uses Taverna as workﬂow engine and we provide several new activities. Our statistical algorithm activities use the Code Execution Framework for the calculations and therefore a cloud based infrastructure is used. g. CSV converter) that can easily be used within the provided AAF-workﬂow. With Taverna the AAF-workﬂow can be adopted and used for many diﬀerent purposes.
The main contribution of the AAF are (a) provide cloud based Taverna activities for classiﬁcation, (b) deﬁne a Taverna workﬂow for the automatic analysis (can easily be extended with already existing activities), and (c) use a generally usable evaluation procedure (k-fold cross validation) for detecting overﬁtting. AAF contributes to the recent developments of productivity enhancement frameworks in eScience applications. The introduced Automatic Analysis Framework (AAF) is able to analyse input data (CSV).