Cosine-Based Clustering Algorithm Approach
Автор: Mohammed A. H. Lubbad, Wesam M. Ashour
Журнал: International Journal of Intelligent Systems and Applications(IJISA) @ijisa
Статья в выпуске: 1 vol.4, 2012 года.
Бесплатный доступ
Due to many applications need the management of spatial data; clustering large spatial databases is an important problem which tries to find the densely populated regions in the feature space to be used in data mining, knowledge discovery, or efficient information retrieval. A good clustering approach should be efficient and detect clusters of arbitrary shapes. It must be insensitive to the outliers (noise) and the order of input data. In this paper Cosine Cluster is proposed based on cosine transformation, which satisfies all the above requirements. Using multi-resolution property of cosine transforms, arbitrary shape clusters can be effectively identified at different degrees of accuracy. Cosine Cluster is also approved to be highly efficient in terms of time complexity. Experimental results on very large data sets are presented, which show the efficiency and effectiveness of the proposed approach compared to other recent clustering methods.
Discrete Cosine Transformation (DCT), Cosine Cluster, Wave Cluster, Wavelet Transformation, Spatial Data, multi-resolution and clusters
Короткий адрес: https://sciup.org/15010093
IDR: 15010093
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