In the next step (Step 4), we carried out the

K-means cluster analysis with the obtained k value of 37.

The statistical technique

K-Means Cluster Analysis enables a classification into four homogeneous groups (Table 4).

A

k-means cluster analysis was also conducted, where the value of k is 3 because three clusters were already determined on the basis of Ward's method.

In line with preceding empirical studies that employed

k-means cluster analysis based on a relative media usage index (Kang et al.

K-means Cluster Analysis Cluster 1 (N = 124) M (z) SD 1.

Overload and taper periods were grouped based on the similarities between the total weekly training loads using

k-means cluster analysis.

Then, the

k-means cluster analysis (k-means), applied when the number of the groups is known a priori, which can be used to refine the results of the hierarchical model and/ or be applied to large volumes of data.

K-means cluster analysis with validation parameters like Partition Index, Separation Index, Xie and Beni's Index and Dunn's Index were applied to free flow speeds to find the optimal number of clusters.

Again, in order to draw inferences from the

k-means cluster analysis we perform a CVA on [W.

As a means of combining the communities into relevant subgroups to be used for efficiently obtaining data for management decision making, we used

K-means cluster analysis (Hartigan and Wong, 1979).

A nonhierarchical

K-means cluster analysis was conducted utilizing the centroids from the Ward's method solution as the initial seed points.

We address the research questions through

k-means cluster analysis, calculation and examination of mean scores for market orientation and overall performance within each identified cluster, and application of t-tests.