Incorporating user constraints into topic-oriented self-organizing maps

Self-organizing map (SOM) algorithm has been applied widely in tasks such as data clustering and visualization. Two major deficiencies of classical SOM are the need of predefined map structure and the lack of hierarchy generation. Several approaches have been devised to tackle these deficiencies. One of our previous works, namely the topic-oriented self-organizing map (TOSOM), tries to remedy the classical SOM by combining topic identification process into the training phase. In this work, we will further expand the learning algorithm of TOSOM by incorporating user's constraints. Both structural and topical constraints which specified by the user could be used to guide the learning process. Preliminary experiments demonstrate improvements over previous algorithm on text categorization task.