Download PDF by Ujjwal Maulik, Lawrence B. Holder, Diane J. Cook: 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 study articles by means of energetic practitioners and major researchers reporting contemporary advances within the box of information discovery. an outline of the sphere, the problems and demanding situations concerned is via assurance of contemporary traits in information mining. this offers the context for the next chapters on equipment and functions. half I is dedicated to the rules of mining kinds of advanced info like timber, graphs, hyperlinks and sequences. a data discovery technique in line with challenge decomposition is usually defined. half II provides very important functions of complicated mining ideas to facts in unconventional and complicated domain names, equivalent to existence sciences, world-wide net, snapshot databases, cyber defense and sensor networks. With an excellent stability of introductory fabric at the wisdom discovery technique, complicated matters and cutting-edge instruments and methods, this e-book might be worthy to scholars at Masters and PhD point in desktop technology, in addition to practitioners within the box.

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S. and L. N. , 1986: Pattern Recognition in Practice II . North Holland, Amsterdam. , A. Srivastava, X. Yang and M. Sarrafzadeh, 2002: Optimal energy aware clustering in sensor networks. Sensors, 2, 258–69. Giles, C. , S. Lawrence and A. C. Tsoi, 2001: Noisy time series prediction using a recurrent neural network and grammatical inference. Machine Learning, 44, 161–83. , 2002: Efficient Frequent Pattern Mining. D. thesis, University of Limburg, Belgium. Goldberg, D. , 1989: Genetic Algorithms: Search, Optimization and Machine Learning.

GAs represent a form of multi-point, stochastic search in complex landscapes. Applications of genetic algorithms and related techniques in data mining include extraction of association rules [85], predictive rules [42, 43, 97], clustering [13, 15, 16, 91, 92, 93], program evolution [117, 126] and web mining [98, 99, 108, 109, 110]. 4 Recent Trends in Knowledge Discovery 31 the real world in a manner similar to biological systems. Their origin can be traced to the work of Hebb [57], where a local learning rule is proposed.

67] Jain, A. K. and R. C. Dubes, 1988: Algorithms for Clustering Data. Prentice-Hall, Englewood Cliffs, NJ. [68] Jensen, F. , 1996: An Introduction to Bayesian Networks. SpringerVerlag, New York, USA. , S. Bandyopadhyay and B. H. , 2005: Special Issue on Distributed and Mobile Data Mining, IEEE Transactions on Systems, Man, and Cybernetics Part B. IEEE. [70] Kargupta, H. and P. , 2001: Advances in Distributed and Parallel Knowledge Discovery. MIT Press. [71] Kargupta. H, R. Bhargava, K. Liu, M.

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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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