NDLI: An Ant-Based Fast Text Clustering Approach Using Pheromone

Content Provider	IEEE Xplore Digital Library
Author	Fuzhi Zhang Yujing Ma Na Hou Hui Liu
Copyright Year	2008
Description	Author affiliation: Coll. of Inf. Sci. & Eng., Yanshan Univ., Qinhuangdao (Fuzhi Zhang; Yujing Ma; Na Hou; Hui Liu)
Abstract	Fast, effective text clustering has been playing an important role in organizing web text information. Due to its self-organization, robustness, flexibility, and visualization, the ant-based clustering approach has been applied to text clustering. In this approach, however, the ant's moving is random, which leads to the convergence speed too slow. Aims at above mentioned problem, an ant-based fast text clustering approach (AFTC) is presented. This approach utilizes pheromone left by ants to avoid ant's moving randomicity, which can make the ant move towards direction which has high pheromone concentration a teach step, and the direction of moving is the orientation where the text vectors are relatively concentration. Compared with the K-means algorithm,the ant-based fast text clustering approach performs the self-organization clustering process without setting clustering center number in advance, which can achieve better performance. The experimental results illustrate that the average time of ant-based fast text clustering approach is faster than that of traditional ant-based text clustering.
Starting Page	385
Ending Page	389
File Size	263267
Page Count	5
File Format	PDF
ISBN	9780769533056
DOI	10.1109/FSKD.2008.649
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-10-18
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Knowledge engineering Visualization Educational institutions ant-based text clustering Organizing Convergence Information science colony similarity Clustering algorithms Euclidean distance Robustness phase variable pheromone Fuzzy systems
Content Type	Text
Resource Type	Article

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