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Fuzzy clustering techniques handle the fuzzy relationships among the data points and with the cluster centers (may be termed as cluster fuzziness). On the other hand, distance measures are important to compute the load of such fuzziness. These are the two important parameters governing the quality of the clusters and the run time. Visualization of multidimensional data clusters into lower dimensions is another important research area to note the hidden patterns within the clusters. This paper investigates the effects of cluster fuzziness and three different distance measures, such as ManHattan distance (MH), Euclidean Distance (ED), and COSine distance (COS) on Fuzzy C-Means (FCM) and Fuzzy k-Nearest Neighborhood (FkNN) clustering techniques, implemented on Iris and extended Wine data.
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