A Rough Set Based Clustering Method by Knowledge Combination
Summary :
This paper presents a rough sets-based method for clustering nominal and numerical data. This clustering result is independent of a sequence of handling object because this method lies its basis on a concept of classification of objects. This method defines knowledge as sets that contain similar or dissimilar objects to every object. A number of knowledge are defined for a data set. Combining similar knowledge yields a new set of knowledge as a clustering result. Cluster validity selects the best result from various sets of combined knowledge. In experiments, this method was applied to nominal databases and numerical databases. The results showed that this method could produce good clustering results for both types of data. Moreover, ambiguity of a boundary of clusters is defined using roughness of the clustering result.
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- IEICE TRANSACTIONS on Information Vol.E85-D No.12 pp.1898-1908
- Publication Date
- 2002/12/01
- Publicized
- Online ISSN
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- Type of Manuscript
- PAPER
- Category
- Databases
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Tomohiro OKUZAKI, Shoji HIRANO, Syoji KOBASHI, Yutaka HATA, Yutaka TAKAHASHI, "A Rough Set Based Clustering Method by Knowledge Combination" in IEICE TRANSACTIONS on Information,
vol. E85-D, no. 12, pp. 1898-1908, December 2002, doi: .
Abstract: This paper presents a rough sets-based method for clustering nominal and numerical data. This clustering result is independent of a sequence of handling object because this method lies its basis on a concept of classification of objects. This method defines knowledge as sets that contain similar or dissimilar objects to every object. A number of knowledge are defined for a data set. Combining similar knowledge yields a new set of knowledge as a clustering result. Cluster validity selects the best result from various sets of combined knowledge. In experiments, this method was applied to nominal databases and numerical databases. The results showed that this method could produce good clustering results for both types of data. Moreover, ambiguity of a boundary of clusters is defined using roughness of the clustering result.
URL: https://global.ieice.org/en_transactions/information/10.1587/e85-d_12_1898/_p
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@ARTICLE{e85-d_12_1898,
author={Tomohiro OKUZAKI, Shoji HIRANO, Syoji KOBASHI, Yutaka HATA, Yutaka TAKAHASHI, },
journal={IEICE TRANSACTIONS on Information},
title={A Rough Set Based Clustering Method by Knowledge Combination},
year={2002},
volume={E85-D},
number={12},
pages={1898-1908},
abstract={This paper presents a rough sets-based method for clustering nominal and numerical data. This clustering result is independent of a sequence of handling object because this method lies its basis on a concept of classification of objects. This method defines knowledge as sets that contain similar or dissimilar objects to every object. A number of knowledge are defined for a data set. Combining similar knowledge yields a new set of knowledge as a clustering result. Cluster validity selects the best result from various sets of combined knowledge. In experiments, this method was applied to nominal databases and numerical databases. The results showed that this method could produce good clustering results for both types of data. Moreover, ambiguity of a boundary of clusters is defined using roughness of the clustering result.},
keywords={},
doi={},
ISSN={},
month={December},}
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TY - JOUR
TI - A Rough Set Based Clustering Method by Knowledge Combination
T2 - IEICE TRANSACTIONS on Information
SP - 1898
EP - 1908
AU - Tomohiro OKUZAKI
AU - Shoji HIRANO
AU - Syoji KOBASHI
AU - Yutaka HATA
AU - Yutaka TAKAHASHI
PY - 2002
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E85-D
IS - 12
JA - IEICE TRANSACTIONS on Information
Y1 - December 2002
AB - This paper presents a rough sets-based method for clustering nominal and numerical data. This clustering result is independent of a sequence of handling object because this method lies its basis on a concept of classification of objects. This method defines knowledge as sets that contain similar or dissimilar objects to every object. A number of knowledge are defined for a data set. Combining similar knowledge yields a new set of knowledge as a clustering result. Cluster validity selects the best result from various sets of combined knowledge. In experiments, this method was applied to nominal databases and numerical databases. The results showed that this method could produce good clustering results for both types of data. Moreover, ambiguity of a boundary of clusters is defined using roughness of the clustering result.
ER -
English
