Journal of Information and Knowledge (Formerly SRELS Journal of Information Management)

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Discovering Frequent Access Patterns in a Digital Library Using Association Mining


Affiliations
1 Department of Computer Science, Madurai Kamaraj University, Madurai 625 021, Tamil Nadu, India
     

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Data Mining, also known as knowledge discovery in databases, has been recognized as a promising new area for database research. Mining frequent item sets in transactional databases, binary transaction tables, time series databases and many other kinds of databases have been an active research topic over the past few years. Frequent access pattern is a special case of sequential pattern in an application database which helps to make effective decisions in the respective problem domain.

Given a large database of book transactions in the library, where each transaction consists of book-id, name of the book, author, and other related fields, the problem is to mine the frequent access patterns of the user from the library databases. The outcome of the findings will help the management to take effective steps that will cater the needs of the user.

Apriori and FP-growth algorithms can mine the complete sets of frequent item sets. These two algorithms were implemented and the performance of the algorithms was studied. The result shows that FP-growth algorithm performs well compared to Apriori.


Keywords

Digital Library, Access Patterns, Apriori, FP-Growth, Algorithm, Mining.
User
About The Authors

G. Arumugam
Department of Computer Science, Madurai Kamaraj University, Madurai 625 021, Tamil Nadu
India

M. Thangaraj
Department of Computer Science, Madurai Kamaraj University, Madurai 625 021, Tamil Nadu
India

P. Shanthi
Department of Computer Science, Madurai Kamaraj University, Madurai 625 021, Tamil Nadu
India


Notifications

  • Srikant (R); Agrawal. (R). Mining Sequential Patterns: Generalizations and Performance Improvements. Research Report RJ 9994, IBM Almaden Research Center, San Jose, California, December 1995.

  • Mining Association Rules with Item Constraints. IBM Almaden Research Centre, San Jose, USA.

  • Helen Pinto; Jiawei Han; Jian Pei; Ke Wang. Multi-dimensional Sequential Pattern Mining, Work Report, Intelligent Database Systems Research Lab, School of Computing Science, Simon Fraser University, Canada.

  • Mobasher (B); Cooley (R); Srivastava (J). Automatic Personalization based on Web Usage Mining. In Communications of the ACM. (43) 8, Aug. 2000.

  • Agrawal (R); Srikant (R). Mining Sequential Patterns. Research Report RJ 9910, IBM Almaden Research Centre, San Jose, California, October 1994.

  • Jiawei Han; Micheline Kamber. Data Mining - Concepts and Techniques. Morgan Kaufmann Publishers, 2001.


Abstract Views: 358

PDF Views: 6




  • Discovering Frequent Access Patterns in a Digital Library Using Association Mining

Abstract Views: 358  |  PDF Views: 6

Authors

G. Arumugam
Department of Computer Science, Madurai Kamaraj University, Madurai 625 021, Tamil Nadu, India
M. Thangaraj
Department of Computer Science, Madurai Kamaraj University, Madurai 625 021, Tamil Nadu, India
P. Shanthi
Department of Computer Science, Madurai Kamaraj University, Madurai 625 021, Tamil Nadu, India

Abstract


Data Mining, also known as knowledge discovery in databases, has been recognized as a promising new area for database research. Mining frequent item sets in transactional databases, binary transaction tables, time series databases and many other kinds of databases have been an active research topic over the past few years. Frequent access pattern is a special case of sequential pattern in an application database which helps to make effective decisions in the respective problem domain.

Given a large database of book transactions in the library, where each transaction consists of book-id, name of the book, author, and other related fields, the problem is to mine the frequent access patterns of the user from the library databases. The outcome of the findings will help the management to take effective steps that will cater the needs of the user.

Apriori and FP-growth algorithms can mine the complete sets of frequent item sets. These two algorithms were implemented and the performance of the algorithms was studied. The result shows that FP-growth algorithm performs well compared to Apriori.


Keywords


Digital Library, Access Patterns, Apriori, FP-Growth, Algorithm, Mining.

References