By Hongmin Cai (auth.), Petra Perner (eds.)
This booklet constitutes the refereed lawsuits of the eleventh commercial convention on facts Mining, ICDM 2011, held in big apple, united states in September 2011.
The 22 revised complete papers offered have been conscientiously reviewed and chosen from a hundred submissions. The papers are prepared in topical sections on facts mining in drugs and agriculture, facts mining in advertising and marketing, info mining for business methods and in telecommunication, Multimedia information Mining, theoretical facets of information mining, information Warehousing, WebMining and knowledge Mining.
Read or Download Advances in Data Mining. Applications and Theoretical Aspects: 11th Industrial Conference, ICDM 2011, New York, NY, USA, August 30 – September 3, 2011. Proceedings PDF
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Additional resources for Advances in Data Mining. Applications and Theoretical Aspects: 11th Industrial Conference, ICDM 2011, New York, NY, USA, August 30 – September 3, 2011. Proceedings
Nowadays, data mining methods with explanation capability are also used for technical domains after more work on advantages and disadvantages of the methods has been done. P. ): ICDM 2011, LNAI 6870, pp. 40–55, 2011. © Springer-Verlag Berlin Heidelberg 2011 How to Interpret Decision Trees? 41 The most preferred method among the methods with explanation capability is decision tree induction method . This method can easily learn a decision tree without heavy user interaction while in neural nets a lot of time is spent on training the net.
Backgrounds and Applications. Advances in Soft Computing, vol. 2. Springer, Heidelberg (in the press, 2011) 7. : Selection of high risk patients with ranked models based on the CPL criterion functions. In: Perner, P. ) ICDM 2010. LNCS, vol. 6171, pp. 432–441. Springer, Heidelberg (2010) 8. : Eksploracja danych oparta na wypukłych i odcinkowo-liniowych funkcjach kryterialnych, Data mining based on convex and piecewise linear criterion functions, Technical University Białystok (2005) (in Polish) 9.
In this paper we describe the behavior of decision tree induction under changing conditions (see Figure 1) in order to give the user a methodology for using decision tree induction methods. The user should be able to detect such influences based on the results of the decision tree induction process. Change in Data Collection Protocol Influence from Outside TIME n n+1 n+2 ... n+4 n+3 Data Stream DS_n+1 DS_n DS_n+3 … DS Data Sampling (DS) Strategy Fig. 1. The Data Collection Problem 3 Decision Tree Induction Based on the Gain Ratio (EntropyBased Measure) The application of decision tree induction methods requires some basic knowledge of how decision tree induction methods work.
Advances in Data Mining. Applications and Theoretical Aspects: 11th Industrial Conference, ICDM 2011, New York, NY, USA, August 30 – September 3, 2011. Proceedings by Hongmin Cai (auth.), Petra Perner (eds.)