Algorithms for clustering data
Algorithms for clustering data
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Identifying genuine clusters in a classification
Computational Statistics & Data Analysis
Resampling Method for Unsupervised Estimation of Cluster Validity
Neural Computation
Testing for Uniformity in Multidimensional Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
A test for spatial randomness based on k-NN distances
Pattern Recognition Letters
Strong consistency of k-parameters clustering
Journal of Multivariate Analysis
Behavior-based clustering and analysis of interestingness measures for association rule mining
Data Mining and Knowledge Discovery
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A method is developed for measuring clustering stability under the removal of a few objects from a set of objects to be partitioned. Measures of stability of an individual cluster are defined as Loevinger's measures of rule quality. The stability of an individual cluster can be interpreted as a weighted mean of the inherent stabilities in the isolation and cohesion, respectively, of the examined cluster. The design of the method also enables us to measure the stability of a partition, that can be viewed as a weighted mean of the stability measures of all clusters in the partition. As a consequence, an approach is derived for determining the optimal number of clusters of a partition. Furthermore, using a Monte Carlo test, a significance probability is computed in order to assess how likely any stability measure is, under a null model that specifies the absence of cluster stability. In order to illustrate the potential of the method, stability measures that were obtained by using the batch K-Means algorithm on artificial data sets and on Iris Data are presented.