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 facts Mining, ICDM 2013, held in big apple, long island, in July 2013. The 22 revised complete papers awarded have been rigorously reviewed and chosen from 112 submissions. the themes diversity from theoretical points of information mining to purposes of information mining, similar to in multimedia info, in advertising, finance and telecommunication, in medication and agriculture, and in method keep watch over, and society.
Read Online or Download Advances in Data Mining. Applications and Theoretical Aspects: 13th Industrial Conference, ICDM 2013, New York, NY, USA, July 16-21, 2013. Proceedings PDF
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Extra resources for Advances in Data Mining. Applications and Theoretical Aspects: 13th Industrial Conference, ICDM 2013, New York, NY, USA, July 16-21, 2013. Proceedings
AAF and the involved ﬁelds – The Automatic Analysis Framework (AAF) helps researchers to (a) ﬁnd new topics of interests or (b) get new insights in their existing research area, thus utility can be increased. The framework has been evaluated on top of real data from the international breath research community [3,4]. The rest of this paper is organized as follows. In Section 2 we provide background information and related work, including productivity in general, the Taverna Workﬂow Management System and our Code Execution Framework.
Traditionally, (full) factorial tests are executed to investigate the eﬀect of the manipulated variables (MV) on the (quality of the) process. Due to their nature, these tests are very time-consuming for batch processes. This paper investigates whether suitable data-driven models for batch optimization and control can be identiﬁed from a more limited set of tests. Based on the results of two case studies, it is concluded that statistical inference models can predict the ﬁnal quality of batches where the MV changes occur at time points not present in the training data, provided they fall inside of the time range used for training.
It is possible to detect overﬁtting if (a) the mean value of the cross validation is much better than the main value of the complete model or (b) the standard deviation is high. The AAF is able to calculate and evaluate a lot of diﬀerent models (see Section 5). Therefore it is upmost important that the system is able to preselect/order the results. 2 System Overview Presently, the Automatic Analysis Framework supports only classiﬁcation with linear regression. In the future we plan to implement predication and clustering with several diﬀerent methods as well.