Advances in Data Mining. Applications and Theoretical - download pdf or read online

By Claus Weihs, Gero Szepannek (auth.), Petra Perner (eds.)

ISBN-10: 364203067X

ISBN-13: 9783642030673

This e-book constitutes the refereed court cases of the ninth commercial convention on facts Mining, ICDM 2009, held in Leipzig, Germany in July 2009.

The 32 revised complete papers awarded have been rigorously reviewed and chosen from one hundred thirty submissions. The papers are prepared in topical sections on information mining in medication and agriculture, info mining in advertising, finance and telecommunication, information mining in technique keep watch over, and society, info mining on multimedia facts and theoretical points of knowledge mining.

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Example text

Ni J i = ∑ d ( xij , µi ) j =1 xij ∈ X i , N i ≠ # X i (1) And d ( x, µ i ) 2 = ( x − µ i ) ( x − µ i ) T (2) Where subscript i represents the cluster or group, µi is the center of the cluster, and d(x, µi) the distance from observation x to the center. Step 3: the new center positions are taken as the initial points for a new iteration starting at step 2. The procedure is repeated until the positions of the centers have minor variations or no longer change between successive iterations. Using this procedure, convergence to a local minimum of function J is guaranteed.

The Euclidian distance between the observed data and its cluster’s center, is used here as a measure of similarity [9]. The procedure can be summarized as follows: Step 1: Random placement of the initial centers. Step 2: Assign each data point to its closest cluster. After all the assignments are completed, redefine the center of the cluster so as to minimize function Ji. Ni J i = ∑ d ( xij , µi ) j =1 xij ∈ X i , N i ≠ # X i (1) And d ( x, µ i ) 2 = ( x − µ i ) ( x − µ i ) T (2) Where subscript i represents the cluster or group, µi is the center of the cluster, and d(x, µi) the distance from observation x to the center.

Data Mining und Wissensentdeckung im Precision Farming - Entwicklung von o¨ konomisch optimierten Entscheidungsregeln zur kleinr¨aumigen Stickstoff-Ausbringung. PhD thesis, TU M¨unchen (2006) 33. : Support vector visualization and clustering using self-organizing map and vector one-class classification. In: Proceedings of the International Joint Conference on Neural Networks, vol. 1, pp. 1,∗ and J. Ricardo Pérez-Correa2 1 Escuela de Ingeniería Industrial. Facultad de Ciencias Económicas y Administrativas.

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Advances in Data Mining. Applications and Theoretical Aspects: 9th Industrial Conference, ICDM 2009, Leipzig, Germany, July 20 - 22, 2009. Proceedings by Claus Weihs, Gero Szepannek (auth.), Petra Perner (eds.)


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