Please use this identifier to cite or link to this item:
|Scopus||Web of Science®||Altmetric|
|Title:||The framework of relative density-based clustering|
|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|
|Series/Report no.:||Communications in Computer and Information Science; 729|
|Conference Name:||International Symposium on Parallel Architectures, Algorithms, and Programming (PAAP) (17 Jun 2017 - 18 Jun 2017 : Haikou, China)|
|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|
|Appears in Collections:||Computer Science publications|
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.