Data Mining: A Book Recommender System Using Frequent Pattern Algorithm
Authors:
Joshua Jonah Vincent
Publication Type: Journal article
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Abstract
The increasing power of computer technology has dramatically increased data collection,
storage, and manipulation ability. As data sets grow in size and complexity, direct “hands-on†data analysis
has increasingly been augmented with indirect, automated data processing.
Over the years, libraries in universities and other educational institutions have gathered a lot of data on books borrowed by students, yet the valuable knowledge embedded in these data has remained untapped.
In many cases, students do not find required books in the library or probably the books have been borrowed by some other students. There are even a lot of books that have never been read by students. In many other cases, library management are faced with the challenge of what book to buy that would maximally benefit the students, and also how to place these books in shelves. There is therefore an urgent need for systems that can help the library management make informed decisions so as to address these issues.
This paper presented a book recommender system that mines frequently hidden and useful patterns from the book library records and make recommendations based on the pattern generated using associated rule mining technique. Data pre-processing and analysis was carried out using frequently pattern growth algorithm to generate frequent patterns.