Durham Medical Books > Data Mining > New PDF release: Advanced Methods for Knowledge Discovery from Complex Data

New PDF release: Advanced Methods for Knowledge Discovery from Complex Data

By Ujjwal Maulik, Lawrence B. Holder, Diane J. Cook

ISBN-10: 1852339896

ISBN-13: 9781852339890

This e-book brings jointly learn articles via energetic practitioners and prime researchers reporting contemporary advances within the box of data discovery. an outline of the sector, the problems and demanding situations concerned is by way of insurance of contemporary developments in info mining. this gives the context for the following chapters on tools and functions. half I is dedicated to the principles of mining types of advanced facts like timber, graphs, hyperlinks and sequences. an information discovery strategy in line with challenge decomposition is usually defined. half II offers vital purposes of complex mining ideas to info in unconventional and intricate domain names, resembling existence sciences, world-wide internet, photograph databases, cyber safeguard and sensor networks. With an outstanding stability of introductory fabric at the wisdom discovery method, complex matters and cutting-edge instruments and strategies, this ebook should be beneficial to scholars at Masters and PhD point in computing device technological know-how, in addition to practitioners within the box.

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Extra info for Advanced Methods for Knowledge Discovery from Complex Data

Sample text

The hyperlinks contain important information which can be utilized for efficient information retrieval. 3 the information that several hyperlinks (edges) point to page A may indicate that A is an authority [76] on some topic. Again, based on the structure of the web graph, it may be possible to identify web communities [41]. A web community is described as a collection of web pages, such that each member of the collection contains many more links to other members in the community than outside it.

Ltering, frequency domain transforms and classification. In [114], Radivojac et al. develop an algorithm for intrusion detection in a supervised framework, where there are far more negative instances than positive (intrusions). A neural-network-based classifier is trained at the base station using data where the smaller class is over-sampled and the larger class is under-sampled [25]. An unsupervised approach to the outlier detection problem in sensor networks is presented in [103], where kernel density estimators are used to estimate the distribution of the data generated by the sensors, and then the outliers are detected depending on a distance-based criterion.

Shawe-Taylor, 2000: An Introduction to Support Vector Machines (and other kernel-based learning methods). Cambridge University Press, UK. [30] Dayhoff, J. , 1990: Neural Network Architectures: An Introduction. Van Nostrand Reinhold, New York. [31] Devijver, P. A. and J. Kittler, 1982: Pattern Recognition: A Statistical Approach. Prentice-Hall, London. , J. Santoyo and J. Dopazo, 2004: Phylogenomics and the number of characters required for obtaining an accurate phylogeny of eukaryote model species.

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Advanced Methods for Knowledge Discovery from Complex Data by Ujjwal Maulik, Lawrence B. Holder, Diane J. Cook

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