Data Mining Apriori Algorithm

Recommended Systems using Collaborative Filtering and Classification Algorithms in Data Mining
Dhwani Shah 2008A7PS097G

Mentor – Mrs. Shubhangi Gawali

BITSC331

2011

1 BITS – Pilani, K.K Birla Goa

INDEX S. No. 1. 2. 3. 4. 5. 6. 7. 8. 9. Topic Introduction to Recommended Systems Problem Statement Apriori Algorithm Pseudo Code Apriori algorithm Example Classification Classification Techniques k-NN algorithm Determine a good value of k References Page No. 3 5 5 7 14 16 19 24 26

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1. Introduction to Recommended Systems
Recommended Systems form a specific type of information filtering system technique that attempts to recommend information items (movies, TV program/show/episode, video on demand, music books, news, images, web pages, scientific literature such as research papers etc.) that are likely to be of interest to the user.. Recommendations can be based on demographics of the users, overall top selling items, or past buying habit of users as a predictor of future items.

Collaborative Filtering (CF)
It is the most successful recommendation technique to date. The basic idea of CF-based algorithms is to provide item recommendations or predictions based on the opinions of other like-minded users. The opinions of users can be obtained explicitly from the users or by using some implicit measures. Collaborative filtering techniques collect and establish profiles, and determine the relationships among the data according to similarity models. The possible categories of the data in the profiles include user preferences, user behavior patterns, or item properties Everyday Examples of Collaborative Filtering... • • • • Bestseller lists Top 40 music lists The “recent returns” shelf at the library Many weblogs

Challenges of collaborative filtering. • The lack of the information would affect the recommendation results. For the relationship mining, new items not-yet-rated or not-yet-labeled can be abandoned in the recommendation processes. •

References:  Agrawal R, Imielinski T, Swami AN. "Mining Association Rules between Sets of Items in Large Databases."SIGM OD. June 1993  Agrawal R, Srikant R. "Fast Algorithms for Mining Association Rules" 1994, Chile, ISBN 1-55860-153-8.  Implementation of Web Usage Mining Using APRIORI and FP Growth Algorithms, B.Santhosh Kumar Department of Computer Science, C.S.I. College of Engineering, K.V.Rukmani Department of Computer Science, C.S.I. College of Engineering.  Mannila H, Toivonen H, Verkamo AI. "Efficient algorithms for discovering association rules."AAAI Workshop on Knowledge Discovery in Databases (SIGKDD). July 1994, Seattle.  Fabrizio Sebastiani. Machine Learning in Automated Text Categorization. ACM Computing Surveys,  Tom Mitchell, Machine Learning. McGraw-Hill, 1997.  Yiming Yang & Xin Liu, A re-examination of text categorization methods. Proceedings of SIGIR, 1999.  Evaluating and Optimizing Autonomous Text Classification Systems (1995) David Lewis. Proceedings of the 18th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval.  Han, Jiawei and Kamber, Micheline. Data Mining: Concepts and Techniques.  Lifshits, Yury. Algorithms for Nearest Neighbor. Steklov Insitute of Mathematics at St. Petersburg. April 2007  Cherni, Sofiya. Nearest Neighbor Method. South Dakota School of Mines and Technology. 26 Acknowledgements I would like to thank Mrs. Shubhangi Gawali for being an excellent mentor and a patient guide throughout this whole learning process 27