Abstract
Finding clusters is a challenging problem especially when the clusters are being of widely varied shapes, sizes, and densities. Density-based clustering methods are the most important due to their high ability to detect arbitrary shaped clusters. However, they are depending on two specified parameters (Eps and Minpts) that define a single density. Moreover, most of these methods are unsupervised, which cannot improve the clustering quality by utilizing a small number of prior knowledge. In this paper we show how background knowledge can be used to bias a density-based clustering method for multi-density data. Experimental results confirm that the proposed method gives better results than other semi-supervised and unsupervised clustering algorithms.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of 2nd International Conference on Knowledge Discovery and Data Mining, pp. 226–231 (1996)
Chen, X., Liu, W., Qiu, K., Lai, J.: APSCAN: a parameter free algorithm for clustering. Pattern Recognit. Lett. 32, 973–986 (2011)
Bohm, C., Plant, C.: Hissclu: a hierarchical density-based method for semi-supervised clustering. In: Proceedings of 11th International Conference on Extending Database Technology (2008)
Ruiz, C., Spiliopoulou, M., Menasalvas, E.: Density-based semi-supervised clustering. Data Min. Knowl. Discov. 21, 345–370 (2010)
Lelis, L., Sander, J.: Semi-supervised density-based clustering. In: Proceedings of 8th IEEE International Conference on Data Mining, pp. 842–847 (2009)
Acknowledgment
The Research was supported in part by Natural Science Foundation of China (No.60903071), National Basic Research Program of China (973 Program, No.2013CB329605), Specialized Research Fund for the Doctoral Program of Higher Education of China, and Training Program of the Major Project of BIT.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Atwa, W., Li, K. (2015). Semi-supervised Clustering Method for Multi-density Data. In: Liu, A., Ishikawa, Y., Qian, T., Nutanong, S., Cheema, M. (eds) Database Systems for Advanced Applications. DASFAA 2015. Lecture Notes in Computer Science(), vol 9052. Springer, Cham. https://doi.org/10.1007/978-3-319-22324-7_33
Download citation
DOI: https://doi.org/10.1007/978-3-319-22324-7_33
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-22323-0
Online ISBN: 978-3-319-22324-7
eBook Packages: Computer ScienceComputer Science (R0)