Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/120109
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Type: Conference paper
Title: The framework of relative density-based clustering
Author: Cui, Z.
Shen, H.
Citation: Parallel architecture, algorithm and programming: 8th International Symposium, PAAP 2017. Proceedings, 2017 / Chen, G., Shen, H., Chen, M. (ed./s), vol.729, pp.343-352
Publisher: Springer
Issue Date: 2017
Series/Report no.: Communications in Computer and Information Science; 729
ISBN: 9789811064418
ISSN: 1865-0929
1865-0937
Conference Name: International Symposium on Parallel Architectures, Algorithms, and Programming (PAAP) (17 Jun 2017 - 18 Jun 2017 : Haikou, China)
Statement of
Responsibility: 
Zelin Cui, and Hong Shen
Abstract: Density-based clustering, using two-phase scheme which consists of an online component and an offline component, is an effective framework for data stream clustering, it can find arbitrarily shaped clusters and capture the evolving characteristic of real-time data streams accurately. However, the clustering has some deficiencies on offline component. Most algorithm don’t adapt to the unevenly distributed data streams or the multi density distribution of the data streams. Moreover, they only consider the density and centroid to connect the adjacent grid and ignore similarity of attribute value between adjacent grids. In this paper, we calculate the similarity of neighboring grids and take the similarity as a weight that affects the connection of the neighboring grids and propose the relative density-based clustering that cluster the grids based on relative difference model that considers the density, centroid and the weight of similarity between adjacent grids, simply, we connect neighboring grids which are the relative small difference to form clusters on offline component. The experimental results have shown that our algorithm apply to the unevenly distributed data streams and has better clustering quality.
Rights: © Springer Nature Singapore Pte Ltd. 2017
RMID: 0030077039
DOI: 10.1007/978-981-10-6442-5_31
Grant ID: http://purl.org/au-research/grants/arc/DP150104871
Appears in Collections:Computer Science publications

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