USP Electronic Research Repository

Information Architecture (IA): Using multidimensional scaling (MDS) and k-means clustering algorithm for analysis of card sorting data

Paea, Sione and Baird, Ross (2018) Information Architecture (IA): Using multidimensional scaling (MDS) and k-means clustering algorithm for analysis of card sorting data. Journal of Usability Studies, 13 (3). pp. 138-157. ISSN 1931-3357

[img] PDF - Accepted Version
Restricted to Repository staff only

Download (684kB)
[img] PDF - Published Version
Restricted to Repository staff only

Download (2MB)


We present a method for visualizing and analyzing card sorting data aiming to develop an in-depth and effective information architecture and navigation structure. One of the well-known clustering techniques for analyzing large data sets is with the k-means algorithm. However that algorithm has yet to be widely applied in analyzing card sorting data sets to measure the similarity between cards and result displays using multidimensional scaling. The multidimensional scaling, which employs particle dynamics to the error function minimization, is a good candidate to be a computational engine for interactive card sorting data. In this paper, we apply the combination of a similarity matrix, a kmeans algorithm, and multidimensional scaling to cluster and calculate an information architecture from card sorting data sets. We chose card sorting to improve an information architecture. We used a spreadsheet table to identify cluster categories and their components. The proposed algorithm handled the overlaps between cards in the card sorting data quite well and displayed the results in a basic layout showing all clusters and card coordinates. For outliers the algorithm allows grouping of single cards to their closest core clusters. The algorithm handled outliers well choosing cards with the strongest similarities from the similarity matrix. We tested the clustering algorithm on real world data sets and compared to other techniques. The results generated clear knowledge on relevant usability issues in visualizing information architecture. The identified usability issues point to a need for a more in-depth search of design solutions that are tailored for the targeted group of people who are struggling with complicated visualizing techniques. This study is for people needed support to easily visualize information architecture from data sets.

Item Type: Journal Article
Uncontrolled Keywords: Information architecture, card sorting, multidimensional scaling, k-means clustering algorithm, analysis, similarity matrix, distance matrix, cross-product matrix
Subjects: Q Science > QA Mathematics
Divisions: Faculty of Science, Technology and Environment (FSTE) > School of Computing, Information and Mathematical Sciences
Depositing User: USP RSC Assistant
Date Deposited: 04 Jun 2018 22:56
Last Modified: 04 Jun 2018 22:56

Actions (login required)

View Item View Item

Document Downloads

More statistics for this item...