Spectral clustering and visualization: a novel clustering of fisher's iris data set.

Content Provider	CiteSeerX
Author	Benson-Putnins, David Bonfardin, Margaret Magnoni, Meagan E. Martin, Daniel Meyer, D. Wessell, Charles D.
Abstract	Abstract. Clustering is the act of partitioning a set of elements into subsets, or clusters, so that elements in the same cluster are, in some sense, similar. Determining an appropriate number of clusters in a particular data set is an important issue in data mining and cluster analysis. Another important issue is visualizing the strength, or connectivity, of clusters. We begin by creating a consensus matrix using multiple runs of the clustering algorithm k-means. This consensus matrix can be interpreted as a graph, which we cluster using two spectral clustering methods: the Fiedler Method and the MinMaxCut Method. To determine if increasing the number of clusters from k to k + 1 is appropriate, we check whether an existing cluster can be split. Finally, we visualize the strength of clusters by using the consensus matrix and the clustering obtained through one of the aforementioned spectral clustering techniques. Using these methods, we then investigate Fisher’s Iris data set. Our methods support the existence of four clusters, instead of the generally accepted three clusters in this data.
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Subject Keyword	Spectral Clustering Consensus Matrix Iris Data Set Novel Clustering Important Issue Fiedler Method Appropriate Number Fisher Iris Data Set Multiple Run Minmaxcut Method Particular Data Set Data Mining Algorithm K-means Cluster Analysis
Content Type	Text
Resource Type	Article