Web Usage Data Clustering using Dbscan algorithm and Set similarities

被引：2

作者：

Santhisree, K. ^{[1
]}

Damodaram, A. ^{[2
]}

Appaji, S. ^{[3
]}

NagarjunaDevi, D. ^{[4
]}

机构：

[1] JNTU, CSE, Hyderabad, Andhra Pradesh, India

[2] JNTU, CSE, UGC ASC, Hyderabad, Andhra Pradesh, India

[3] Arora Engn Coll, Hyderabad, Andhra Pradesh, India

[4] CMEC, CSE, Hyderabad, Andhra Pradesh, India

来源：

PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON DATA STORAGE AND DATA ENGINEERING (DSDE 2010) | 2010年

关键词：

Sequence; Web usage data; set approximations; set similarity; rough sets;

D O I：

10.1109/DSDE.2010.14

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Web usage mining is the application of data mining techniques to web log data repositories. It is used in finding the user access patterns from web access log. User page visits are sequential in nature. In this paper we presented new Rough set Dbscan clustering algorithm which identifies the behavior of the users page visits, order of occurrence of visits. Web data Clusters are formed using the rough set Similarity Upper Approximations. We present the experimental results on MSNBC web navigation dataset, and proved that Rough set Dbscan clustering has better efficiency and performance clustering in web usage mining is finding the groups which share common interests compared to Rough set agglomerative clustering.

引用

页码：220 / 224

页数：5

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