1306 A New Clustering Method for Network-Type Data in Engineering Information
Shumei Kameyama, Makoto Uchida, Susumu Shirayama · The proceedings of the JSME annual meeting · 2007
Network-type data increases in engineering information, since information becomes to be structured by hyperlink. In order to extract knowledge from such data, many methods to detect cluster structure in network-type data have been developed. However, it is difficult to classify or identify the clusters detected in the network-type data, and such clusters are not sufficiently exploited. In this paper, we propose a method for identifying clusters detected by a certain clustering method. One of the clustering methods is utilized to divide a network-type data into clusters. The clusters are identified by another clustering method.