Home   >   CSC-OpenAccess Library   >    Manuscript Information
Full Text Available

(256.64KB)
This is an Open Access publication published under CSC-OpenAccess Policy.
performance improvement and efficient approach for mining periodic sequential acess patterns
D. Vasumathi, Dr. A. Govardhan , K.Venkateswara Rao
Pages - 358 - 370     |    Revised - 26-11-2009     |    Published - 26-12-2009
Volume - 3   Issue - 5    |    Publication Date - November 2009  Table of Contents
MORE INFORMATION
KEYWORDS
password, key agreement scheme, verifier-typed, password guessing attack, stolen verifier attack.
ABSTRACT
Abstract Surfing the Web has become an important daily activity for many users. Discovering and understanding web users’ surfing behavior are essential for the development of successful web monitoring and recommendation systems. To capture users’ web access behavior, one promising approach is web usage mining which discovers interesting and frequent user access patterns from web usage logs. Web usage mining discovers interesting and frequent user access patterns from web logs. Most of the previous works have focused on mining common sequential access patterns of web access events that occurred within the entire duration of all web access transactions. However, many useful sequential access patterns occur frequently only during a particular periodic time interval due to user browsing behaviors and habits. It is therefore important to mine periodic sequential access patterns with periodic time constraints. In this paper, we propose an efficient approach, known as TCSMA (Temporal Conditional Sequence Mining Algorithm), for mining periodic sequential access patterns based on calamander-based periodic time constraint. The calamander-based periodic time constraints are used for describing real-life periodic time concepts such as the morning of every weekend. The mined periodic sequential access patterns can be used for temporal-based personalized web recommendations. The performance of the proposed TCSMA is evaluated and compared with a modified version of Web Access Pattern Mine for mining periodic sequential access patterns. Keywords: Periodic Sequential Access Patterns, Web Access Patterns, Association Rule, Web Log Mining, TCSM&WAPM Algorithm
CITED BY (0)  
1 Google Scholar
2 Academic Journals Database
3 ScientificCommons
4 Academic Index
5 CiteSeerX
6 refSeek
7 iSEEK
8 ResearchGATE
9 Libsearch
10 Bielefeld Academic Search Engine (BASE)
11 OpenJ-Gate
12 Scribd
13 WorldCat
14 SlideShare
15 PDFCAST
16 PdfSR
17 Chinese Directory Of Open Access
18 Free-Books-Online
1 Kosala R., and Blockeel H., (2000). Web Mining Research: A Survey. In ACM SIGKDD Explorations, Vol. 2, pp. 1-15.
2 Ganter B., and Wille R., (1999). Formal Concept Analysis: Mathematical Foundations. Springer, Heidelberg, 1999.
3 Cooley R., Mobasher B., and Srivastava J. (1999). Data Preparation for Mining World Wide Web Browsing Patterns. In Journal of Knowledge and Information Systems, Vol. 1, No. 1.
4 Agrawal R., and Srikant R. (1995). Mining Sequential Patterns. In Proceedings of the 11th International Conference on Data Engineering, Taipei, Taiwan, pp. 3-14.
5 Srikant R., and Agrawal R. (1996). Mining Sequential Patterns: Generalizations and Performance Improvements. In Proceedings of the 5th International Conference on Extending Database Technology (EDBT), Avignon, France, pp. 3-17.
6 Pei J., Han J., Mortazavi-asl B., and Zhu H. (2000). Mining Access Patterns Efficiently from Web Logs. In Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD ‘00), Kyoto, Japan, pp. 396-407.
7 Lu H., Luo Q., Shun Y.K., (2003). Extending a Web Browser with Client-Side Mining. In Proceedings of the 5th Asia Pacific Web Conference (APWeb), pp. 166-177.
8 Ozden B., Ramaswamy S., and Silberschatz A. (1998). Cyclic Association Rules. In Proceedings of the 14th International Conference on Data Engineering, pp. 412-421.
9 Ramaswamy S., Mahajan S., and Silberschatz A. (1998). On the Discovery of Interesting Patterns in Association Rules. In Proceedings of the 24th International Conference on. on Very Large Data Bases, New York, USA, pp. 368-379.
Professor D. Vasumathi
- India
rochan44@gmail.com
Dr. Dr. A. Govardhan
- India
Dr. K.Venkateswara Rao
- India