Means Clustering Algorithm

The k-Means clustering algorithm is used to identify k solutions that are used to create the surrogate models. From the possible m solutions in the archive, k centres pi. /xfc are obtained. 

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Figure 16 Results of applying the fuzzy k-means clustering algorithm to the data from Table 9. The values in parenthesis indicate the membership function for each object relative to group A...

Hartigan JA, Wong MA (1979) Algorithm AS 136 A K-means clustering algorithm. J Roy Stat Soc C Appl Stat 28 100-108... 

The traditional hierarchical and nonhierarchical (e.g., fc-means) clustering algorithms [69] have a number of drawbacks that require caution in their implementation for time series data. The hierarchical clustering algorithms assume an implicit parent-child relationship between the members of a cluster which may not be relevant for time series data. However, they can provide good initial estimates of patterns that may exist in the data set. The fc-means algorithm requires the estimate of the number of clusters (i.e., k) and its solution depends on the initial assignments as the optimization... 

Lin, T.-H. and Tsai, K-C. (2003) Implementing the Fisher s discriminant ratio in fe-means clustering algorithm for feature selection and data set trimming. /. Chem. Inf. Comput. Sci., 44, 76-87. 

Figure 4.17 Results of appyling the fuzzy k-means clustering algorithm to the data from...

Fisher s Discriminant Ratio in a k-Means Clustering Algorithm for Feature Selection and Data Set Trimming. 

Because of its relationship to SOM, we will describe in greater detail the K-means clustering algorithm. The criterion function is the total squared distance of the data items to their nearest cluster centroids. 

For the purpose of this study we used. fiT-means clustering, which is a method of vector quantization.. fif-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells. X-means clustering algorithm can be described as follows ... 

The other major variant of clustering minimizes some objective function of the intersample distances. The results are also dependent on user-selected parameters (e.g. the number of clusters and the type of the distance measure used). However, these results become more realistic if we allow overlap between clusters, i.e. if we accept that the samples can be fuzzy, having memberships in all clusters. Bezdek s fuzzy c-means clustering algorithm is the most popular of such clustering methods. Neither clustering... 

Still, despite the use ofthe K-means clustering algorithm by both techniques of autonomous coordination, each technique differs from the other in terms of whether or not robots rely on any particular member ofthe collective to perform the bulk to the computation required for coordination. The first teclmique presented is that of coordination performed with the hierarchically structured collective. Following that, coordination performed with the horizontally stractuied collective is described. 

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