Abstract
Recently various clustering approaches have been developed for web pages clustering optimization. Traditional methods take the vector model as their free-text analytical basis. However these algorithms cannot perform well on these problems which involving many Ecommerce information objectives. A novel approach based on the ECC vector space model FCM clustering algorithm is proposed to deal with these problems in this paper. By introducing Ecommerce concept (ECC) model, the Automatic Constructing Concept algorithm is proposed at first. Through the ACC algorithm and fields keywords table, the Ecommerce concept objects are established automatically. The ECC-Based Fuzzy Clustering (EFCM) is used to divide web pages into the different concept subsets. The experiment has compared it with Kmeans, Kmedoid, and Gath-Geva clustering algorithm, and results demonstrate the validity of the new algorithm. According to classification performance, the EFCM algorithm shows that it can be a clustering method for the Ecommerce semantic information searching in Internet.
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© 2007 Springer-Verlag Berlin Heidelberg
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Ouyang, Y., Ling, Y., Zhu, A. (2007). ECC-Based Fuzzy Clustering Algorithm. In: Cao, BY. (eds) Fuzzy Information and Engineering. Advances in Soft Computing, vol 40. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71441-5_41
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DOI: https://doi.org/10.1007/978-3-540-71441-5_41
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-71440-8
Online ISBN: 978-3-540-71441-5
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