Fuzzy Set Based Clustering Algorithm of Web Text

Hong Xin Wan; Yun Peng

doi:10.4028/www.scientific.net/AMM.678.19

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 678Fuzzy Set Based Clustering Algorithm of Web Text

Fuzzy Set Based Clustering Algorithm of Web Text

Abstract:

Web text exists non-certain and non-structure contents ,and it is difficult to cluster the text by normal classification methods. We propose a web text clustering algorithm based on fuzzy set to increase the computing accuracy with the web text. After abstracting the key words of the text, we can look it as attributes and design the fuzzy algorithm to decide the membership of the words. The algorithm can improve the algorithm complexity of time and space, increase the robustness comparing to the normal algorithm. To test the accuracy and efficiency of the algorithm, we take the comparative experiment between pattern clustering and our algorithm. The experiment shows that our method has a better result.

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Periodical:

Applied Mechanics and Materials (Volume 678)

Pages:

19-22

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.678.19

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Online since:

October 2014

Authors:

Hong Xin Wan, Yun Peng

Keywords:

Clustering Analysis, Fuzzy Set, Fuzzy Similarity Matrix, Membership Function

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References

[1] Kannan S R, Ramathilagam S, Chung P C. Effective fuzzy c-means clustering algorithms for data clustering problems. Expert Systems with Applications, Vol. 39, No. 7, pp.6292-6300, (2012).

DOI: 10.1016/j.eswa.2011.11.063

Google Scholar

[2] He W. Improving user experience with case-based reasoning systems using text mining and Web 2. 0. Expert Systems with Applications, Vol. 40, No. 2, pp.500-507, (2013).

DOI: 10.1016/j.eswa.2012.07.070

Google Scholar

[3] Agrawal R, Batra M. A detailed study on text mining techniques. International Journal of Soft Computing and Engineering (IJSCE), pp.2231-2307, (2013).

Google Scholar

[4] Huang H C, Chuang Y Y, Chen C S. Multiple kernel fuzzy clustering. Fuzzy Systems, Vol. 20, No. 1, pp.120-134, (2012).

DOI: 10.1109/tfuzz.2011.2170175

Google Scholar

[5] Thomas B, Raju G. A novel unsupervised fuzzy clustering method for preprocessing of quantitative attributes in association rule mining. Information Technology and Management, Vol. 15, No. 1, pp.9-17, (2014).

DOI: 10.1007/s10799-013-0168-7

Google Scholar

[6] Agnihotri D, Verma K, Tripathi P. Pattern and Cluster Mining on Text Data. Communication Systems and Network Technologies (CSNT), 2014 Fourth International Conference on. IEEE, pp.428-432, (2014).

DOI: 10.1109/csnt.2014.92

Google Scholar