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Biblioqrafik təsvir | Aliguliyev , R.M. Finding compact and well-separated clusters: Clustering using silhouette coefficients / R.M. Aliguliyev , A.M. Bagirov // Pattern Recognition. - 2023. - N: 135. - P. 109-144. | Annotasiya | Finding compact and well-separated clusters in data sets is a challenging task. Most clustering algorithms
try to minimize certain clustering objective functions. These functions usually reflect the intra-cluster
similarity and inter-cluster dissimilarity. However, the use of such functions alone may not lead to the
finding of well-separated and, in some cases, compact clusters. Therefore additional measures, called cluster validity indices, are used to estimate the true number of well-separated and compact clusters. Some
of these indices are well-suited to be included into the optimization model of the clustering problem.
Silhouette coefficients are among such indices. In this paper, a new optimization model of the clustering
problem is developed where the clustering function is used as an objective and silhouette coefficients
are used to formulate constraints. Then an algorithm, called CLUSCO (CLustering Using Silhouette COefficients), is designed to construct clusters incrementally. Three schemes are discussed to reduce the computational complexity of the algorithm. Its performance is evaluated using fourteen real-world data sets
and compared with that of three state-of-the-art clustering algorithms. Results show that the CLUSCO
is able to compute compact clusters which are significantly better separable in comparison with those
obtained by other algorithms. | Elektron variant | Elektron variant |
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