Paea, Sione and Katsanos, Christos and Bulivou, Gabiriele (2022) Information architecture: using K - means clustering and the best merge method for open card sorting data analysis. Interacting with Computers, 33 (6). pp. 670-689. ISSN 0953-5438
Preview |
PDF
- Published Version
Available under License Creative Commons Attribution. Download (2MB) | Preview |
Abstract
Open card sorting is a well-established method for discovering how people understand and categorize information. This paper addresses the problem of quantitatively analyzing open card sorting data using the K-means algorithm. Although the K-means algorithm is effective, its results are too sensitive to initial category centers. Therefore, many approaches in the literature have focused on determining suitable initial centers. However, this is not always possible, especially when the number of categories is increased. This paper proposes an approach to improve the quality of the solution produced by the K-means for open card sort data analysis. Results show that the proposed initialization approach for K-means outperforms existing initialization methods, such as MaxMin, random initialization and K-means++. The proposed algorithm is applied to a real-world open card sorting dataset, and, unlike existing solutions in the literature, it can be used with any number of participants and cards.
| Item Type: | Journal Article |
|---|---|
| Subjects: | Q Science > QA Mathematics Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | School of Information Technology, Engineering, Mathematics and Physics (STEMP) |
| Depositing User: | Gabiriele Bulivou |
| Date Deposited: | 30 Aug 2022 04:07 |
| Last Modified: | 30 Aug 2022 04:07 |
| URI: | https://repository.usp.ac.fj/id/eprint/13640 |
Actions (login required)
 |
View Item |