ClusteringFuzzy c-meansMissing dataThe fuzzy c-means algorithm is a useful tool for clustering real s-dimensional data. Typically, each observation (or datum) consists of numerical values for s features such as height, length, etc. In some cases, data sets contain vectors that are missing one...
Fuzzy clustering is meant to provide a richer means for representing data structure. Data weighted approach for fuzzy clustering This section first presents the data weighted fuzzy clustering objective function, and then derives the necessary updated equations. Numerical experiments This section evaluates ...
Fuzzy C-Means Clustering Fuzzy c-means(FCM) is a data clustering technique where each data point belongs to a cluster to a degree that is specified by a membership grade. The FCM algorithm starts with an initial guess for the cluster centers, which represent the mean location of each cluster...
This paper transmits a FORTRAN-IV coding of the fuzzy c -means (FCM) clustering program. The FCM program is applicable to a wide variety of geostatistical data analysis problems. This program generates fuzzy partitions and prototypes for any set of numerical data. These partitions are useful for...
Numerical data based fuzzy models perform well in precision but not necessarily in interpretation. To utilize the expert knowledge and numerical data in a fuzzy model synchronously, this paper proposed a hybrid fuzzy c-means (FCM) clustering algorithm and Fuzzy Network (FN) method-based model for...
Clustering Data of Mixed Categorical and Numerical Type With Unsupervised Feature Learning IEEE Access., 3 (c) (2015), pp. 1605-1616 Google Scholar 9 Huang Z. Clustering Large Data Sets with Mixed Numeric and Categorical Values; 1997. Google Scholar 10 Kim DW Fuzzy clustering of categorical da...
R J,Hathaway,J C,Bezdek 摘要: The problem of clustering a real s-dimensional data set X={x(1 ),,,x(n)} subset R(s) is considered. Usually, each observation (or datum) consists of numerical values for all s features (such as height, length, etc.), but sometimes data sets can...
This paper presents a new type of clustering algorithm by using cosine correlation and a tolerance vector. We aim to handle uncertain data with some range or missing values with the typical clustering algorithm of fuzzy c-means with cosine correlation (FCM-C). To handle such data, we introduce...
Li D, Gu H and Zhang L 2010 A fuzzy c-means clustering algorithm based on nearest- neighbor intervals for incomplete data Expert Systems with Applications 37 6942-7Li Dan,Gu Hong,Zhang Li-yong.A fuzzy c-means clus- tering algorithm based on nearest-neighbor intervals for incomplete data[J...
For the unemployment time series, when the number of partitioned intervals is 9, the values of RMSE and NE of Chen's model with our approach are 0.18 and 2.14, respectively, but with the fuzzy c-means clustering-based partition method (Wang13) are 0.24 and 2.78, with the Gath-Geva ...