2021-09-17T14:27:36Zhttps://tsukuba.repo.nii.ac.jp/oaioai:tsukuba.repo.nii.ac.jp:000154392021-03-02T08:48:52ZAlgorithms of nonlinear document clustering based on fuzzy multiset model宮本, 定明Mizutani, KiyotakaInokuchi, RyoMiyamoto, Sadaaki© 2008 Wiley Periodicals, Inc.application/pdfFuzzy multiset is applicable as a model of information retrieval because it has the mathematical\nstructure that expresses the number and the degree of attribution of an element simultaneously.\nTherefore, fuzzy multisets can be used also as a suitable model for document clustering. This\npaper aims at developing clustering algorithms based on a fuzzy multiset model for document\nclustering. The standard proximity measure of the cosine correlation is generalized in the multiset\nmodel, and two nonlinear clustering techniques are applied to the existing clustering methods.\nOne introduces a variable for controlling cluster volume sizes; the other one is a kernel trick used\nin support vector machines. Moreover, clustering by competitive learning is also studied. When\nthe kernel trick has been used the classification configuration of data in a high-dimensional feature\nspace is visualized by self-organizing maps. Two numerical examples, which use an artificial data\nand real document data, are shown and effects of the proposed methods are discussed.Wiley Periodicals2008-01engjournal articlehttp://hdl.handle.net/2241/98371https://tsukuba.repo.nii.ac.jp/records/1543910.1002/int.202630884-8173AA10683914International journal of intelligent systems232176198https://tsukuba.repo.nii.ac.jp/record/15439/files/IJIS_23-2.pdfapplication/pdf837.5 kB2013-12-19