Profiling Academic Library Patrons using K-means and X-means Clustering

Abstract

Information technology is now used very often, especially by individuals born between 1982 and 2002 (the Millennial generation). The academic library, which from its beginnings has been a storehouse for information through collections, is becoming less attractive for Millennials because of the influence of information technology. This study aimed to use k-means and x-means clustering algorithms to identify the characteristics of academic library patrons, particularly Millennial patrons. K-means is a well-known algorithm due to its simplicity, while x-means is a relatively new algorithm for performing clustering and provides the capability to determine an optimal number of clusters, the number of cluster that minimizes differences within each cluster and maximizes differences between clusters. In this study, data were collected using questionnaires, both in online and offline forms. A total of 935 responses were collected. The results show that k-means performs better than x-means since it results in a lower Davies-Bouldin index value. However, x-means provides better descriptions of the patrons’ behavior on each cluster. Both k-means and x-means clustering methods create five clusters based on the behavior of academic library patrons. One of the clusters resulting from k-means and x-means also confirms that not all patrons come to the academic library for the book collection; they come because of invitations from friends or to use internet services.

Keywords

• academic library
• clustering
• k-means
• x-means