González-Toledo, Marggie D.

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    A comparison in cluster validation techniques
    (2004) González-Toledo, Marggie D.; Acuña-Fernández, Edgar; College of Arts and Sciences - Sciences; Lorenzo, Edgardo; Saito, Tokuji; Department of Mathematics; Ortiz, Jorge L.
    Clustering may be defined as a process that aims to find partitions of similar objects. It is an unsupervised recognition procedure since there are no predefined classes that indicate grouping properties in the data set. Researchers have extensively studied clustering since it arise in many application domains in engineering, social science, and biology. The basic problem in clustering is to decide the optimal number of clusters, or partitions, that fits a data set. Sometimes the clusters obtained after we applying some clustering algorithms does not represent the structure that the data set really has. For this reason we need quantitative measures to evaluate the results of a clustering algorithm. This task is named Cluster Validity. This thesis includes a description about the clustering algorithms, and its validation techniques. Our main goal is to identify which cluster validation techniques is most efficient in order to divide a given data set. In this research it was done applying seven cluster validation techniques along with three clustering algorithms on ten different data sets. The results were obtained using the R programming language and environment for statistical computing. This software can be download from the page http://www.r-project.org/ [1].