916 resultados para Data Mining and its Application


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Mode of access: Internet.

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Mode of access: Internet.

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Mode of access: Internet.

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At head of title: Institute for International and Foreign Trade Law of the Georgetown University Law Center, Washington, D.C.

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This paper proposes a novel application of fuzzy logic to web data mining for two basic problems of a website: popularity and satisfaction. Popularity means that people will visit the website while satisfaction refers to the usefulness of the site. We will illustrate that the popularity of a website is a fuzzy logic problem. It is an important characteristic of a website in order to survive in Internet commerce. The satisfaction of a website is also a fuzzy logic problem that represents the degree of success in the application of information technology to the business. We propose a framework of fuzzy logic for the representation of these two problems based on web data mining techniques to fuzzify the attributes of a website.

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The edge-to-edge matching crystallographic model has been used to predict all the orientation relationships (OR) between crystals that have simple hexagonal close packed (HCP) and body-centered cubic (BCC) structures. Using the critical values for the interatomic spacing misfit along the matching directions and the cl-value mismatch between matching planes, the model predicted all the four common ORs, namely the Burgers OR, the Potter OR, the Pitsch-Schrader OR and the Rong Dunlop OR, together with the corresponding habit planes. Taking the c(H)/a(H) and a(H)/a(B) ratios as variables, where H and B denote the HCP and BCC structures respectively, the model also predicted the relationship between these variables and the four ORs. These predictions are perfectly consistent with the published experimental results. As was the case in the FCC/BCC system, the edge-to-edge matching model has been shown to be a powerful tool for predicting the crystallographic features of diffusion-controlled phase transformations. (C) 2004 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

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A model for the crystallography and morphology of diffusion-controlled phase transformations - edge-to-edge matching - has been used to predict the orientation relationships (OR) and habit planes of precipitates Mg17Al12 in Mg-Al alloy, Mg24Y5 in Mg-Y alloy and alpha-Mn in Mg-Mn alloy. Based on the crystal structures and lattice parameters only, the model predicts that the possible ORs between Mg17Al12 and Mg matrix are the near Burgers OR, the Potter OR, the Gjonnes-Ostmoe OR and the Crawley OR. In the Mg-Y alloy, the OR between Mg24Y5 precipitates and the Mg matrix is predicted to be the Burgers OR only. The model also predicts that there are no reproducible ORs between alpha-Mn and Mg in the Mg-Mn alloy. Combining the edge-to-edge matching model and W. Zhang's Deltag approach, the habit plane and side facets of the precipitate for each OR can be determined. All the predicted ORs and the corresponding habit planes in Mg-Al and Mg-Y alloys agree very well with the experimental results. (C) 2004 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

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Frequent Itemsets mining is well explored for various data types, and its computational complexity is well understood. There are methods to deal effectively with computational problems. This paper shows another approach to further performance enhancements of frequent items sets computation. We have made a series of observations that led us to inventing data pre-processing methods such that the final step of the Partition algorithm, where a combination of all local candidate sets must be processed, is executed on substantially smaller input data. The paper shows results from several experiments that confirmed our general and formally presented observations.

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This paper presents load profiles of electricity customers, using the knowledge discovery in databases (KDD) procedure, a data mining technique, to determine the load profiles for different types of customers. In this paper, the current load profiling methods are compared using data mining techniques, by analysing and evaluating these classification techniques. The objective of this study is to determine the best load profiling methods and data mining techniques to classify, detect and predict non-technical losses in the distribution sector, due to faulty metering and billing errors, as well as to gather knowledge on customer behaviour and preferences so as to gain a competitive advantage in the deregulated market. This paper focuses mainly on the comparative analysis of the classification techniques selected; a forthcoming paper will focus on the detection and prediction methods.