Cluster stability; Cluster validation; Quality assessment; Stability-based validation of clustering
A problem one faces in clustering is to decide the optimal partitioning of the data into clusters. In this context visualization of the data set is a crucial verification of the clustering results. In the case of large multidimensional data sets (e.g., more than three dimensions) effective visualization of the data set is cumbersome. Moreover the perception of clusters using available visualization tools is a difficult task for humans that are not accustomed to higher dimensional spaces. The procedure of evaluating the results of a clustering algorithm is known under the term cluster validity. Cluster validity consists of a set of techniques for finding a set of clusters that best fits natural partitions (of given datasets) without any a priori class information. The outcome of the clustering process is validated by a cluster validity index.
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