抄録
This paper proposes a document clustering algorithm based on formal concept analysis. This novel clustering algorithm uses mathematically order information among data. Conventional clustering algorithms deal with numeric data so that every non-numeric data are transformed to numeric form. Proposed algorithm generates word vectors from document data and define a context table. Then it calculates a concept lattice based on the word inclusion and exclusion relation among data. The experiments are conducted using 100 and 200 documents derived from Reuters-21578 text database. The result shows that our algorithm can classified the documents and also can assess the significance of each cluster.
