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 publication brings jointly learn articles by means of lively practitioners and prime researchers reporting contemporary advances within the box of data discovery. an outline of the sector, taking a look at the problems and demanding situations concerned is via insurance of modern traits in info mining. this offers the context for the next chapters on equipment and purposes. half I is dedicated to the rules of mining kinds of complicated information like timber, graphs, hyperlinks and sequences. an information discovery procedure according to challenge decomposition is additionally defined. half II provides vital purposes of complicated mining innovations to facts in unconventional and intricate domain names, corresponding to existence sciences, world-wide net, picture databases, cyber defense and sensor networks. With a great stability of introductory fabric at the wisdom discovery strategy, complicated concerns and cutting-edge instruments and methods, this booklet can be worthwhile to scholars at Masters and PhD point in computing device technological know-how, in addition to practitioners within the box.

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Martin and S. Lytinen, 1995: FAQ finer: A case-based approach to knowledge navigation. Working notes of the AAAI Spring Symposium: Information gathering from heterogeneous, distributed environments, AAAI Press, Stanford University, 69–73. Han, J. and M. Kamber, 2000: Data Mining: Concepts and Techniques. Morgan Kaufmann, San Francisco, USA. Hartigan, J. , 1975: Clustering Algorithms. John Wiley. , 1994: Neural Networks, A Comprehensive Foundation. McMillan College Publishing Company, New York. References 37 [57] Hebb, D.

K then terminate. Otherwise continue from Step 2. Note that if the process does not terminate at Step 4 normally, then it is executed for a maximum fixed number of iterations. It has been shown in [119] that the K-means algorithm may converge to values that are not optimal. Also global solutions of large problems cannot be found within a reasonable amount of computation effort [122]. It is because of these factors that several approximate methods, including genetic algorithms and simulated annealing [15, 16, 91], are developed to solve the underlying optimization problem.

Journal of Artificial Intelligence Research, 16, 321–57. , J. C. Hou and L. Sha, 2004: Dynamic clustering for acoustic target tracking in wireless sensor networks. IEEE Transactions on Mobile Computing, 3, 258–71. [27] Chiang, D. , L. R. Chow and Y. F. Wang, 2000: Mining time series data by a fuzzy linguistic summary system. Fuzzy Sets and Systems, 112, 419–32. , K. Sugawara, and T. Watanabe, 2001: Classification and function estimation of protein by using data compression and genetic algorithms.

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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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