Abstract
A fuzzy clustering method is proposed to cluster objects and classes based on the cooccurrence matrix that represents the cooccurrence relationship of the objects and the classes. It is a type of method known as a graph spectral method that reduces the problem to an eigenvalue problem and successively extracts the clusters. A method based on the similarity matrix is applied to the cooccurrence matrix and is extended to hierarchical fuzzy clustering. This method obtains the cluster information of the class simultaneously with object clustering. As an application example of this clustering method, we present data retrieval by key words. Since clustering extracts the overall data structure to some degree, the retrieval is robust in noisy data similar to Latent Semantic Indexing. Fuzzy clustering performs object-level retrieval because the detailed information lost in hard clustering is preserved.
| Original language | English |
|---|---|
| Pages (from-to) | 10-19 |
| Number of pages | 10 |
| Journal | Electronics and Communications in Japan, Part II: Electronics (English translation of Denshi Tsushin Gakkai Ronbunshi) |
| Volume | 84 |
| Issue number | 8 |
| DOIs | |
| Publication status | Published - Aug 2001 |
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All Science Journal Classification (ASJC) codes
- Electrical and Electronic Engineering
Cite this
Fuzzy clustering based on cooccurrence matrix and its application to data retrieval. / Inoue, Kohei; Urahama, Kiichi.
In: Electronics and Communications in Japan, Part II: Electronics (English translation of Denshi Tsushin Gakkai Ronbunshi), Vol. 84, No. 8, 08.2001, p. 10-19.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Fuzzy clustering based on cooccurrence matrix and its application to data retrieval
AU - Inoue, Kohei
AU - Urahama, Kiichi
PY - 2001/8
Y1 - 2001/8
N2 - A fuzzy clustering method is proposed to cluster objects and classes based on the cooccurrence matrix that represents the cooccurrence relationship of the objects and the classes. It is a type of method known as a graph spectral method that reduces the problem to an eigenvalue problem and successively extracts the clusters. A method based on the similarity matrix is applied to the cooccurrence matrix and is extended to hierarchical fuzzy clustering. This method obtains the cluster information of the class simultaneously with object clustering. As an application example of this clustering method, we present data retrieval by key words. Since clustering extracts the overall data structure to some degree, the retrieval is robust in noisy data similar to Latent Semantic Indexing. Fuzzy clustering performs object-level retrieval because the detailed information lost in hard clustering is preserved.
AB - A fuzzy clustering method is proposed to cluster objects and classes based on the cooccurrence matrix that represents the cooccurrence relationship of the objects and the classes. It is a type of method known as a graph spectral method that reduces the problem to an eigenvalue problem and successively extracts the clusters. A method based on the similarity matrix is applied to the cooccurrence matrix and is extended to hierarchical fuzzy clustering. This method obtains the cluster information of the class simultaneously with object clustering. As an application example of this clustering method, we present data retrieval by key words. Since clustering extracts the overall data structure to some degree, the retrieval is robust in noisy data similar to Latent Semantic Indexing. Fuzzy clustering performs object-level retrieval because the detailed information lost in hard clustering is preserved.
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U2 - 10.1002/ecjb.1045
DO - 10.1002/ecjb.1045
M3 - Article
VL - 84
SP - 10
EP - 19
JO - Electronics and Communications in Japan, Part II: Electronics (English translation of Denshi Tsushin Gakkai Ronbunshi)
JF - Electronics and Communications in Japan, Part II: Electronics (English translation of Denshi Tsushin Gakkai Ronbunshi)
SN - 8756-663X
IS - 8
ER -