Analysis of Web Log Data Mining Based on Improved Fuzzy Clustering Algorithm

Chuan Qi Chen

doi:10.4028/www.scientific.net/AMR.760-762.1896

Paper Titles

A Infrared Small Moving Object Extraction Method in the Context of Complex Background Motion
p.1879

On Real-Time Modeling of 3D Terrain Database
p.1884

Application of Similarity Algorithm in the Assessment of the Students Curriculum Design
p.1888

Coordination Algorithm Design of the Distributed Storage Proxy Servers for Streaming Media
p.1892

Analysis of Web Log Data Mining Based on Improved Fuzzy Clustering Algorithm
p.1896

A Method on Process Planning Decision Based on Genetic Algorithm
p.1902

A Hybird Virtual Machine Placement Aglrithm for Virtualized Desktop Infrastructure
p.1906

Curve-Skeleton Extraction Using Appropriate Threshold Optimization
p.1911

A General Vertex Partition Refinement Algorithm for Graph Isomorphism
p.1919

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 760-762Analysis of Web Log Data Mining Based on Improved...

Analysis of Web Log Data Mining Based on Improved Fuzzy Clustering Algorithm

Abstract:

Fuzzy clustering analysis is a clustering algorithm based on function best practices, technology and optimal cost function using calculus. Fuzzy clustering, each sample is no longer belong to a class, but belong to a certain degree of membership of each class. In this paper, Web log sequential pattern mining knowledge gained, and visitors have the same browsing mode access to cutting the interaction of users with the Web information space. The paper presents analysis of Web log data mining based on improved fuzzy clustering algorithm. The experiment demonstrates the improved algorithm has better scalability.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 760-762)

Pages:

1896-1901

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.760-762.1896

Citation:

Cite this paper

Online since:

September 2013

Authors:

Chuan Qi Chen

Keywords:

Data Mining (DM), Fuzzy Clustering Analysis, Web Mining

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] BO Yao-jun, ZHANG Xiao, SHANG Ai-jun, ZHANG Xiong, WEN Zhong-ming, Self-repair Succession of Plant Community by Quantum-inspired Fuzzy, JCIT, Vol. 6, No. 8, p.161 ~ 167, (2011).

Google Scholar

[2] Zhen Liu, XianFeng Yang, An application model of fuzzy clustering analysis and decision tree algorithms in building web mining, JDCTA, Vol. 6, No. 23, p.492 ~ 500, (2012).

DOI: 10.4156/jdcta.vol6.issue23.56

Google Scholar

[3] Junfei Chen, Zeyuan Huang, , Huiming Wang, Drought Situation Evaluation of Crops Based on Integrated Gray Fuzzy-Clustering Method, JCIT, Vol. 7, No. 19, p.211 ~ 219, (2012).

DOI: 10.4156/jcit.vol7.issue19.25

Google Scholar

[4] J. Srivastava, R. Cooley, M. Deshpande and P. Tan, Web usage mining: discovery and applications of usage patterns from web data. SIGKDD Explorations, 1(2): 12–23, (2000).

DOI: 10.1145/846183.846188

Google Scholar

[5] Dimitrios Pierrakos Georgios Paliouras Web Usage Mining as a Tool for Personalization: A Survey. Kluwer Academic Publishers, 2003: 311~372.

Google Scholar

[6] Taufik Fuadi Abidin, Alim Misbullah, , Muhammad Subianto, Determining Features of Web Documents and Building a Web Classifier using Support Vector Machine, AISS, Vol. 3, No. 10, p.401 ~ 408, (2011).

DOI: 10.4156/aiss.vol3.issue10.49

Google Scholar