When there are only a few unique values of the mean, then k-means clustering can also be used.

One simple method is performing k-means clustering over all the vectors.

Here, we explore the utility of one such method - fuzzy k-means clustering.

Now that the initial centers have been chosen, proceed using standard k-means clustering.

In particular, Mount has worked on the k-means clustering problem, nearest neighbor search, and point location.

We implemented the algorithm to perform three successive cycles of fuzzy k-means clustering.

As a robustly converging alternative to the k-means clustering it is also used for cluster analysis.

Despite the advantages of fuzzy k-means clustering discussed above, the method also has a number of limitations.

In this example, the result of k-means clustering (the right figure) contradicts the obvious cluster structure of the data set.

K-means clustering is an algorithm for classifying and grouping genes based on pattern into K groups.

A key difference between these algorithms is that fuzzy k-means clustering requires no a priori information about the dataset.

Netanyahu has co-authored highly cited research papers on nearest neighbor search and k-means clustering.

Mount has worked on developing practical algorithms for k-means clustering, a problem known to be NP-hard.

One of the most frequently used techniques in geodemographic segmentation is the widely known k-means clustering algorithm.

Hierarchical clustering, and k-means clustering are widely used techniques in microarray analysis.

Fuzzy k-means clustering is well suited to identifying conditionally coexpressed genes for a number of reasons.

Thus, the results of fuzzy k-means clustering are naturally suited for biologists to use in an intuitive and physiologically meaningful way.

Despite these limitations, the unique advantages of fuzzy k-means clustering make the technique a valuable tool for gene-expression analysis.

The continuous clusters identified by fuzzy k-means clustering present a challenge in visualizing the clustering results.

In contrast, fuzzy k-means clustering appears to be less sensitive to over-fitting, because the genes are not forced to belong to only a single cluster.

The majority of the clusters identified by fuzzy k-means clustering were not statistically enriched for known transcription factor binding sites.

The overlapping clusters identified by fuzzy k-means clustering also present more comprehensive groups of conditionally coregulated genes.

We believe that fuzzy k-means clustering will be a useful complement to other computational methods commonly used to analyze gene-expression data.

It supports multiple algorithms for data mining like K-means clustering, association rule mining, decision tree analysis and regression.

In data mining, k-means++ is an algorithm for choosing the initial values (or "seeds") for the k-means clustering algorithm.