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To Enhance Frequent Closed Pattern Tree using Fuzzy Clustering of Personalized Web-Log in Big Data

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Authors:
Sapana Kumari, Vikram Garg
10.5120/ijca2016910267

Sapana Kumari and Vikram Garg. To Enhance Frequent Closed Pattern Tree using Fuzzy Clustering of Personalized Web-Log in Big Data. International Journal of Computer Applications 144(5):21-24, June 2016. BibTeX

@article{10.5120/ijca2016910267,
	author = {Sapana Kumari and Vikram Garg},
	title = {To Enhance Frequent Closed Pattern Tree using Fuzzy Clustering of Personalized Web-Log in Big Data},
	journal = {International Journal of Computer Applications},
	issue_date = {June 2016},
	volume = {144},
	number = {5},
	month = {Jun},
	year = {2016},
	issn = {0975-8887},
	pages = {21-24},
	numpages = {4},
	url = {http://www.ijcaonline.org/archives/volume144/number5/25175-2016910267},
	doi = {10.5120/ijca2016910267},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

In recent time data mining on big data is very tedious task in current scenario because huge data cannot be handling in the memory with different format type of data. In distributed environment web pages access by the user having some patterns, these patterns are merging and finding closed frequent set of web pages. Now do the Fuzzy C-Means clustering of web page access pattern tree. If user need next request page in advance then it search only partial web data not in whole web data. So that this research utilized a personalized weighted recommendation system based on user's interest with less execution time.

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Keywords

Web Usage Mining; Closed Sequential Patterns; Sequence Tree; Web Log Data; Fuzzy Clustering.