Work place: Computer Engineering Dept. Faculty of engineering, Islamic university of gaza (IUG), Gaza, Palestine
E-mail: Engmlubbad@gmail.com
Website:
Research Interests: Computational Science and Engineering, Software, Software Construction, Software Engineering
Biography
Mohammed A. H. Lubbad was born in Gaza, Palestine in 1987. He received the B.Sc. degree from Islamic University of Gaza, in 2010.
In 2010, he joined the Graduate Studies Program of Faculty of Engineering at Islamic University of Gaza at Gaza Strip, in Palestine, as a M.Sc. Student. From 2010 until now, he is working as software engineering at Al-Tariq company for systems and projects in Gaza, Palestine.
By Mohammed A. H. Lubbad Wesam M. Ashour
DOI: https://doi.org/10.5815/ijisa.2012.01.07, Pub. Date: 8 Feb. 2012
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.
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