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

The Millennial generation (also known as Generation Y) is the group of individuals born between 1982 and 2002 (Kotz, 2016). Members of this generation have unique characteristics. For example, many Millennials do not wear watches because their cell phones display the time. Furthermore, rather than using physical photograph albums, Millennials store their photographs on Facebook, Instagram, and other social media platforms. Millennials enjoy using technology. Indeed, they are the first generation to have become dependent on technology (Smith & Nichols, 2015).  They live in an age when they can instantly access  whatever information they want,  for example, academic data or information through their smartphones (Maiers, 2017) as part of their use of existing technology. According to Maiers (2017), social networks have become one of the strongest motivators for Millennials, rather than interactions in the real world.

In this age, information technology is one of the most frequently used tools in life, including for academic libraries because most have chosen to integrate technology into their information content (Walton, 2014). Many academic library patrons surveyed for the study reported that obtaining information from the internet is easier than having to search in a library. It is also easier because not everything patrons need is available in libraries. Moreover, technology is one of the most commonly used communication tools, and the majority of users who use it to communicate are Millennials (Maiers, 2017).

Learning methods must continually adapt to engage and educate this generation (Nicholas, 2008). Millennials tend to have a different learning method than previous generations. Millennials prefer to have skills and creativity in arts, games, video lectures, field trips, and other activities that do not depend only on books and theories. They tend to work beyond required working hours and have less social time (DeVaney, 2015). Moreover, Millennials are fluent in the uses of technology or perhaps even dependent on it (Nicholas, 2008).

To keep pace with and adjust to evolutions in Millennials’ learning methods, academic libraries must be able to convert some of their traditional services into digital services. Academic libraries are transitioning from a collections-based model to a broader services-based model (Gleason, 2018). Library services, most of which are printed books, must be converted into digital services augmented by free internet services and other offerings to accommodate Millennials’ needs. Fulfilling users’ needs may increase customer satisfaction and affect an institution’s success. User satisfaction may be achieved by identifying service quality attributes and their effects on user satisfaction (Zuna et al., 2016).

As mentioned above, Millennials often look for academic references on the internet rather than physically searching in a library. In one study, 79.5% of college students reported that they are experts at using the internet to search for information efficiently and effectively, but only 56.4% said that they are skilled in using the college library (Lippincott, 2012). Thus, academic libraries must understand the characteristics of Millennials in order to create an environment that is attractive to them; for example, in providing books Millennials need and promoting such services, libraries can attract the attention of patrons to persuade them to continue using books (Lippincott, 2012). Each customer may have a different perspective on the attributes that affect his preferences since customer preferences can be influenced by the completeness of the product/service attributes and the transaction process (Suzianti et al., 2015).

At the present time, academic libraries are providing number of services that Millennials would find attractive and may not able to find in online sources such as data management, information about digital scholarship, copyright management, citation management, open educational resources, and others (Dempsey & Malpas, 2018).

Millennials students exhibit a number of common characteristics: They are more focused on achievement, they prefer to question everything and use all means available to get information, and they use technology not only to find information on the internet but also for typing notes in class (Freeman et al., 2014). Currently, data integration and analysis are still rarely used to support decision-making, although many academic libraries have applied technology to obtain various reader information. Tremendous amounts of collected data remain to be analyzed in a simple analysis such as correlation (Wang et al., 2011). Thus, in the present study, in order to obtain information about the characteristics of students who use the library often, the most suitable method was clustering because it can be used to identify unique distributions or patterns in data and discover groups of data (Halkidi et al., 2001).

Table 1 Changes in the function of the library

Source: (Dempsey & Malpas, 2018)

Clustering methods are unsupervised classification methods aimed at facilitating the discovery process by combining a set of objects to create a collection of data subjects that have homogenous groups (Bader et al., 2006; Padmaja et al., 2008). The cluster members in one group have maximum similarities but minimum similarities with other cluster group members. Clustering is different from classification. Clustering is the segmenting of data into a group, while classification segments some data by assigning it into groups (Chen & Chen, 2006). The quality of clustering data depends on how high the intra-class similarities are and how low the inter-class similarities are. A common measure of cluster accuracy is the Euclidean distance. Computational time may also be used as a measure of cluster performance (Aparna & Mydhili, 2016).

By using a data mining method such as clustering, it is possible to discover different behaviors of patrons and possibly use those behaviors to determine whether a library’s service and collection match Millennials’ learning methods. Two methods used to conduct the data integration are k-means and x-means clustering. K-means clustering is a data mining algorithm that divides n objects into k clusters so that the members of one cluster have high similar characteristics while the members of different clusters are dissimilar (Ahmar et al., 2018). X-means clustering is an extension of k-means clustering that refines the clustering by continuously splitting the cluster until the selection criterion is reached.

The aim of the present study was to profile the behavior of academic library patrons, particularly patrons who are categorized as members of the Millennial generation, by comparing the clusters resulting from the k-means and x-means clustering methods.