By Ujjwal Maulik, Lawrence B. Holder, Diane J. Cook
This publication brings jointly examine articles via energetic practitioners and major researchers reporting contemporary advances within the box of information discovery. an summary of the sector, taking a look at the problems and demanding situations concerned is via assurance of modern developments in information mining. this offers the context for the next chapters on equipment and purposes. half I is dedicated to the principles of mining types of complicated info like timber, graphs, hyperlinks and sequences. a data discovery process in response to challenge decomposition can also be defined. half II offers vital functions of complex mining suggestions to facts in unconventional and complicated domain names, akin to existence sciences, world-wide internet, photo databases, cyber protection and sensor networks. With an exceptional stability of introductory fabric at the wisdom discovery technique, complicated matters and state of the art instruments and methods, this ebook may be priceless to scholars at Masters and PhD point in machine technology, in addition to practitioners within the box.
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Extra info for Advanced Methods for Knowledge Discovery from Complex Data
In , 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 . An unsupervised approach to the outlier detection problem in sensor networks is presented in , 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.
36                  Sanghamitra Bandyopadhyay and Ujjwal Maulik Proc. 4th Int. Symp. on Large Spatial Databases (SSD’95), Portland, Maine, 67–82. , G. Piatetsky-Shapiro and P. Smyth, 1996: The KDD process for extracting useful knowledge from volumes of data. Communications of the ACM , 39, 27–34. Flake, G. , S. Lawrence and C. L. Giles, 2000: Eﬃcient identiﬁcation of the web communities. Proceedings on the 6th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 150–160.
48] have studied the theoretical aspects of this problem with application to energy optimization. They illustrate an optimal algorithm for clustering the sensor nodes such that each cluster (that is characterized by a master) is balanced and the total distance between the sensor nodes and the master nodes is minimized. Some other approaches in this regard are available in [26, 135]. Algorithms for clustering the data spread over a sensor network are likely to play an important role in many sensor-network-based applications.