NDLI: The Improved Algorithm of ART2 in Data Mining

Content Provider	IEEE Xplore Digital Library
Author	Li Liangun Zhang Bin Che Yuanyuan
Copyright Year	2009
Abstract	Clustering analysis is an important research topic in data mining field, and it is one of main task of data mining. Adaptive Resonance Theory (ART) neural network is an effective method to realize clustering. But the classical ART2 network has some shortcoming and insufficiency in data clustering application. The classical ART2 network must designate p alert parameters before the network training, the configuration of this parameter has a direct impact on the network clustering result. The classical ART2 uses the "winner takes all" competition rule, in general only considers winning neuron information, but neglects other useful neuron information in the output layer. The classical ART2 network output is essentially one-dimension structure, is unable to manifest the whole relation to entire input mode space. By improving the structure of ART2, ample considering amplitude information of mining object, which can decrease the requirement of vigilance parameter and earn cluster result with administrative level structure .The validity of this improve is verified by experimental result.
Starting Page	177
Ending Page	180
File Size	361837
Page Count	4
File Format	PDF
ISBN	9780769536040
DOI	10.1109/DBTA.2009.105
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-04-25
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Stability Subspace constraints Neurons Amplitude Information Data Mining Data mining Clustering ART Neural Networks Information science Databases Neural networks Channel hot electron injection Clustering algorithms Resonance
Content Type	Text
Resource Type	Article

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