By Topic

Dynamic and adaptive self organizing maps applied to high dimensional large scale text clustering

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Zhonghui Feng ; Inst. of Comput. Software, Xi''an Jiaotong Univ., Xi''an, China ; Junpeng Bao ; Junyi Shen

The self organizing maps(SOM) has been used as a tool for mapping high-dimensional input data into a low-dimensional feature map, which has significant advantages for text clustering applications. In this paper, a novel dynamic and adaptive SOM algorithm applied to high dimensional large scale text clustering is proposed. The characteristic feature of this novel neural network model is its dynamic architecture which grows (when the similarity between input pattern (text vector) and weight vector of the winning node is smaller than a given threshold) during its training process to find the inherent topology structure of the document set. By using unsupervised competitive learning in network, the weight vectors of the winning node and its nearest neighbors are adjusted adaptively (where learning rate is related to similarity in amended learning rule) in this algorithm. The results of the experiments indicated that the algorithm successfully improve quality of text clustering and learning speed of neural network.

Published in:

Software Engineering and Service Sciences (ICSESS), 2010 IEEE International Conference on

Date of Conference:

16-18 July 2010