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Vydunas Saltenis 《Journal of Global Optimization》2006,35(4):625-635
There exist many data clustering algorithms, but they can not adequately handle the number of clusters or cluster shapes.
Their performance mainly depends on a choice of algorithm parameters. Our approach to data clustering and algorithm does not
require the parameter choice; it can be treated as a natural adaptation to the existing structure of distances between data
points. The outlier factor introduced by the author specifies a degree of being an outlier for each data point. The outlier
factor notion is based on the difference between the frequency distribution of interpoint distances in a given dataset and
the corresponding distribution of uniformly distributed points. Then data clusters can be determined by maximizing the outlier
factor function. The data points in dataset are divided into clusters according to the attractor regions of local optima.
An experimental evaluation of the proposed algorithm shows that the proposed method can identify complex cluster shapes. Key
advantages of the approach are: good clustering properties for datasets with comparatively large amount of noise (an additional
data points), and an absence of important parameters which adequate choice determines the quality of results. 相似文献
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