Modular structure generation by greedy network-growing algorithm
Ryotaro Kamimura, Haruhiko Takeuchi · 2005
In this paper, we propose a new method to generate modular structures. In the method, the number of elements, that is, the number of competitive units is gradually increased. To control a process of module generation, we introduce two kinds of information, that is, unit and modular information. Unit information represents information content obtained by individual elements in all modules. On the other hand, modular information is information content obtained by each module. We try to increase both types of information simultaneously. We applied our method to two classification problems: random data classification and Web data classification. In both cases, we observed that modular structures were automatically generated.