Knowledge discovery of digital library subscription by RFC itemsets

Abstract

Purpose

The paper aims to understand the book subscription characteristics of the students at each college and help the library administrators to conduct efficient library management plans for books in the library. Unlike the traditional association rule mining (ARM) techniques which mine patterns from a single data set, this paper proposes a model, recency-frequency-college (RFC) model, to analyse book subscription characteristics of library users and then discovers interesting association rules from equivalence-class RFC segments.

Design/methodology/approach

A framework which integrates the RFC model and ARM technique is proposed to analyse book subscription characteristics of library users. First, the author applies the RFC model to determine library users’ RFC values. After that, the author clusters library users’ transactions into several RFC segments by their RFC values. Finally, the author discovers RFC association rules and analyses book subscription characteristics of RFC segments (library users).

Findings

The paper provides experimental results from the survey data. It shows that the precision of the frequent itemsets discovered by the proposed RFC model outperforms the traditional approach in predicting library user subscription itemsets in the following time periods. Besides, the proposed approach can discover interesting and valuable patterns from library book circulation transactions.

Research limitations/implications

Because RFC thresholds were assigned based on expert opinion in this paper, it is an acquisition bottleneck. Therefore, researchers are encouraged to automatically infer the RFC thresholds from the library book circulation transactions.

Practical implications

The paper includes implications for the library administrators in conducting library book management plans for different library users.

Originality/value

This paper proposes a model, the RFC model, to analyse book subscription characteristics of library users.

Citation

Weng, C.-H. (2016), "Knowledge discovery of digital library subscription by RFC itemsets", The Electronic Library, Vol. 34 No. 5, pp. 772-788. https://doi.org/10.1108/EL-06-2015-0086