Deepa S. Deshpande

Work place: MGM’s Jawaharlal Nehru Engineering College, Aurangabad, 431003, India



Research Interests: Computer systems and computational processes, Computer Vision, Image Compression, Image Manipulation, Image Processing, Data Mining, Database Management System, Data Compression, Data Structures and Algorithms


Deepa S. Deshpande has obtained her Bachelor of computer engineering in 1995 and M.Tech in computer engineering in 2006 from Pune University. Currently she is pursuing Phd. in the area of image mining at SRTMU University. Presently she is working as Associate Professor in Computer Science and Engineering department of MGM’s Jawaharlal Nehru Engineering College, Aurangabad, Maharashtra, India. She has industrial experience of 1 year and teaching experience of 16 years. Her area of interest is Database Management Systems, Data Warehousing, Data Mining, Computer Vision, Image Mining. She is life member of professional societies like CSI, ISTE and QCFI. 

Author Articles
A Novel Approach for Association Rule Mining using Pattern Generation

By Deepa S. Deshpande

DOI:, Pub. Date: 8 Oct. 2014

Data mining has become a process of significant interest in recent years due to explosive rate of the accumulation of data. It is used to discover potentially valuable implicit knowledge from the large transactional databases. Association rule mining is one of the well known techniques of data mining. It typically aims at discovering associations between attributes in the large databases. The first and the most influential traditional algorithm for association rule discovery is Apriori. Multiple scans of database, generation of large number of candidates item set and discovery of interesting rules are the main challenging issues for the improvement of Apriori algorithm. Therefore in order to decrease the multiple scanning of database, a new method of association rule mining using pattern generation is proposed in this paper. This method involves three steps. First, patterns are generated using items from the transaction database. Second, frequent item set is obtained using these patterns. Finally association rules are derived. The performance of this method is evaluated with the traditional Apriori algorithm. It shows that behavior of the proposed method is much more similar to Apriori algorithm with less memory space and reduction in multiple times scanning of database. Thus it is more efficient than the traditional Apriori algorithm.

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