Closing Remarks and Future Directions
Matthew J. Kyan, Paisarn Muneesawang, Kambiz Jarrah, Ling Guan · 2014
The focal points of the book lay in the design and development of two novel models for unsupervised learning or data clustering, based on dynamic Self-Organization: namely, the self-organizing tree map (SOTM) and The Self-Organizing Hierarchical Variance Map (SOHVM). This chapter summarizes the main properties and recommendations in the use of such models, and discusses some potential directions for future research and application. The real advantage of creating a self-organized clustering as opposed to most other clustering methods, lies in the availability of the resulting topological map. Mining the topology, as opposed to assuming one through imposing predetermined lattice, can be leveraged for very specific tasks. The chapter focuses on three major categories of task: namely, dynamic navigation through information repositories; knowledge-assisted visualization; and path-based trajectory analysis. In each category, there is a common Theme-where there is topology, there is context, and context can assist in conveying or extracting knowledge.