Advances In Data Mining: Applications in Image Mining, by Petra P. (ed.)

By Petra P. (ed.)

This e-book constitutes the completely refereed post-proceedings of the 4th commercial convention on information Mining, ICDM 2004, held in Leipzig, Germany on July 2004.The convention used to be concerned about complicated facts mining purposes in picture mining, medication and bioinformatics, administration and environmental regulate, and telecommunications. The 18 revised complete papers offered have been conscientiously chosen in the course of rounds of reviewing and development. The papers are prepared in topical sections on case-based reasoning, photo mining, functions in procedure keep an eye on and coverage, clustering and organization ideas, telecomunications, and medication and biotechnology.

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This paper outlines the problem first and then presents the IBR system developed to estimate the partial pressure of carbon dioxide and presents some initial results obtained with it. 2 Ocean’s Carbon Dioxide Budget The carbon dioxide concentration in the atmosphere is governed primarily by the exchange of carbon dioxide with the ocean. The atmospheric content of carbon dioxide is increasing at an annual rate of about 3 billion tons which corresponds to one half of the annual emission rate of approximately 6 billion tons from fossil fuel combustion.

M. S. Corchado, and J. Aiken The IBR system developed incorporates a novel Cooperative Maximum Likelihood Hebbian Learning model for the data clustering and retrieval and a radial-bases function neural network for instance adaptation and forecast. A case-based reasoning system solves new problems by adapting solutions that were used to solve old problems. The case base holds a number of problems with their corresponding solutions. Once a new problem arises, the solution to it is obtained by retrieving similar cases from the case base and studying the similarity between them.

4 The Mahalanobis Distance Function The Mahalanobis distance function base of the covariance matrix [5] in the feature space X is defined on the The Mahalanobis distance function takes into account the linear dependencies in the pairs of the features and When the covariance matrix is equal to the unit matrix then the Mahalanobis distance function is reduced to the Euclidean distance functions (3). The Mahalanobis neighbourhood of the point in the feature space X is defined through using the distances (7) in a manner similar to (11).

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