Sequential pattern mining on library transaction data
Imas Sukaesih Sitanggang,Nor Azura Husin,Anita Agustina,Naghmeh Mahmoodian +3 more
- 15 Jun 2010
- Vol. 1, pp 1-4
TL;DR: Results of the work in applying the sequential pattern mining algorithm namely AprioriAll on a library transaction dataset are presented, which can help library in providing book recommendation to students, conducting book procurement based on readers need, and managing books layout.
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Abstract: Application of data mining techniques in library data results interesting and useful patterns that can be used to improve services in university libraries. This paper presents results of the work in applying the sequential pattern mining algorithm namely AprioriAll on a library transaction dataset. Frequent sequential patterns containing book sequences borrowed by students are generated for minimum supports 0.3, 0.2, 0.15 and 0.1. These patterns can help library in providing book recommendation to students, conducting book procurement based on readers need, as well as managing books layout.
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Citations
An Empirical Study of Application of Data Mining Techniques in Library System
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TL;DR: This paper addresses the applications of data mining in library to extract useful information from the huge data sets and providing analytical tool to view and use this information for decision making processes by taking real life examples.
Collaborative Book Recommendation Based on Readers' Borrowing Records
Liu Xin,Haihong E,Song Junde,Song Meina,Tong Junjie +4 more
- 13 Dec 2013
TL;DR: This paper constructs the ratings from the readers' borrowing records to enable the collaborative filtering algorithms and shows that linearly combining a set of CF algorithms increases the accuracy and outperforms any single CF algorithms.
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Developing a novel recommender network-based ranking mechanism for library book acquisition
Fan Wu,Ya Han Hu,Ping Rong Wang +2 more
TL;DR: The results show that the proposed ranking mechanism can facilitate effective book-acquisition decisions in libraries and can be used to improve the accuracy of book recommendations.
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Community Detection Based on Readers' Borrowing Records
Liu Xin,Haihong E,Song Junde +2 more
- 20 Aug 2013
TL;DR: It is found that most of the people only borrow one or two books once a time and the interval between two successive borrowing records is usually shot as half a month, so by constructing the reader-reader similarity network, it have some characteristics: scale-free distribution, the small-world effect and strong community structure.
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Sequential pattern mining in educational data: The application context, potential, strengths, and limitations
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TL;DR: In this article , the authors identify that SPM is suitable for mining learning behaviors, analyzing and enriching educational theories, evaluating the efficacy of instructional interventions, generating features for prediction models, and building educational recommender systems.
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SPADE: An Efficient Algorithm for Mining Frequent Sequences
TL;DR: SPADE is a new algorithm for fast discovery of Sequential Patterns that utilizes combinatorial properties to decompose the original problem into smaller sub-problems, that can be independently solved in main-memory using efficient lattice search techniques, and using simple join operations.