Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
GTM: A Principled Alternative to the Self-Organizing Map
ICANN 96 Proceedings of the 1996 International Conference on Artificial Neural Networks
Information Visualization: Perception for Design
Information Visualization: Perception for Design
Understanding user behavior with new mobile applications
The Journal of Strategic Information Systems
Seeding the survey and analysis of research literature with text mining
Expert Systems with Applications: An International Journal
On the development of a technology intelligence tool for identifying technology opportunity
Expert Systems with Applications: An International Journal
Integrating Data Warehouses with Web Data: A Survey
IEEE Transactions on Knowledge and Data Engineering
Mobile Service Innovation and Business Models
Mobile Service Innovation and Business Models
Expert Systems with Applications: An International Journal
Global data mining: An empirical study of current trends, future forecasts and technology diffusions
Expert Systems with Applications: An International Journal
Visualizing knowledge and information: an introduction
Knowledge and Information Visualization
| Hi-index | 12.05 |
Recently, due to the explosive increase of services, firms have faced with challenges to analyze patterns and trends in services in an intuitive but objective ways. The notion of service map can be adapted to this end. Maps, in general, have been receiving a great deal of attention because of their potential as visualization tools that can allow people to visualize massive amounts of information. Specifically, the generative topographic mapping through time (GTM-TT) algorithm is suitable for dynamic analysis since GTM-TT provides a time-based clustering and change path. In response, this study proposes an approach for developing and using GTM-TT service maps consisting of a service clustering map and a service sequence map for analyzing service trends. The proposed approach, broadly, is comprised of four steps: (1) the construction of a database, (2) data preprocessing, (3) development of a GTM-TT service map, and (4) interpretation. The proposed approach is expected to aid in the identification of dynamic service trends.