Fuzzy clustering for categorical multivariate data Export

@proceedings{citeulike:1435104, abstract = {This paper proposes a new fuzzy clustering algorithm for categorical multivariate data. The conventional fuzzy clustering algorithms form fuzzy clusters so as to minimize the total distance from cluster centers to data points. However, they cannot be applied to the case where only cooccurrence relations among individuals and categories are given and the criterion to obtain clusters is not available. The proposed method enables us to handle that kind of data set by maximizing the degree of aggregation among clusters. The clustering results by the proposed method show similarity to those of correspondence analysis or Hayashi's (1952) quantification method Type III. Numerical examples show the usefulness of our method}, author = {Oh, Chi-Hyon and Honda, K. and Ichihashi, H.}, citeulike-article-id = {1435104}, citeulike-linkout-0 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=944403}, journal = {IFSA World Congress and 20th NAFIPS International Conference, 2001. Joint 9th}, keywords = {fccm}, pages = {2154--2159 vol.4}, posted-at = {2007-07-05 07:06:07}, priority = {2}, title = {Fuzzy clustering for categorical multivariate data}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=944403}, volume = {4}, year = {2001} }

TY - CONF ID - citeulike:1435104 L3 - citeulike-article-id:1435104 N2 - This paper proposes a new fuzzy clustering algorithm for categorical multivariate data. The conventional fuzzy clustering algorithms form fuzzy clusters so as to minimize the total distance from cluster centers to data points. However, they cannot be applied to the case where only cooccurrence relations among individuals and categories are given and the criterion to obtain clusters is not available. The proposed method enables us to handle that kind of data set by maximizing the degree of aggregation among clusters. The clustering results by the proposed method show similarity to those of correspondence analysis or Hayashi's (1952) quantification method Type III. Numerical examples show the usefulness of our method JF - IFSA World Congress and 20th NAFIPS International Conference, 2001. Joint 9th EP - 2159 vol.4 TI - Fuzzy clustering for categorical multivariate data VL - 4 SP - 2154 KW - fccm AU - Oh, Chi-Hyon AU - Honda, K AU - Ichihashi, H PY - 2001/// UR - http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=944403 ER -