Abstract
The goals of bioinformatics are the solving of biological questions and the active driving of the work of biologists by offering search and analysis methods for research data. The internet brings us distributed environments in which we can access the databases of various research groups. However, a very large quantity of data always causes trouble, creating crucial problems, such as problems with the search for and analysis of data in these distributed environments. Data clustering can be a solution when searching for data. However, this task is very tedious because its execution time is directly proportional to the volume of data. In this paper we propose a distributed clustering scenario and a modified K-means algorithm for the efficient clustering of biological data, and demonstrate the enhancement in performance that it brings.
This study was supported by a grant of the Korea Health 21 R&D Project, Ministry of Health & Welfare, Republic of Korea. (0412-MI01-0416-0002).
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 subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Crignon, I., Grzybek, S., Staedtler, F., Masiello, A., Dressman, M., Taheri, F., Stock, R., Lenges, E., Pitarelli, R., Genesio, F., Reinhardt, M.: An Architecture for Standardization and Management of Gene Expression Data in a Global Organization. In: ECCB (European Conference on Computational Biology), Paris, France, September 27-30 (2003)
Zhang, B., Formaml, G.: Distributed Data Clustering Can Be Efficient and Exact, Software Technology Laboratory HPLaboratories, Palo Alto HPL-2000-158 (December 4, 2000)
Albert, O., Zomaya, Y.: Tarek E1-Ghazawi.:Parallel and Distributed Computing for Data Mining. IEEE Concurrency 7(4), 11–13 (1999)
Zhang, Y.-F., Mao, J.-L., Xiong, Z.-Y.: An efficient clustering algorithm. 2003 International Conference on Machine Learning and Cybernetics 1, 261–265 (2003)
Martin, W., Horton, R.M.: MageBuilder: A Schema Translation Tool for Generating MAGE-ML from Tabular Microarray Data. In: 2003 IEEE Proceedings of Computational Systems Bioinformatics (CSB 2003), pp. 431–432 (2003)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Jeong, J., Ryu, B., Shin, D., Shin, D. (2007). Integration of Distributed Biological Data Using Modified K-Means Algorithm. In: Washio, T., et al. Emerging Technologies in Knowledge Discovery and Data Mining. PAKDD 2007. Lecture Notes in Computer Science(), vol 4819. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77018-3_46
Download citation
DOI: https://doi.org/10.1007/978-3-540-77018-3_46
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-77016-9
Online ISBN: 978-3-540-77018-3
eBook Packages: Computer ScienceComputer Science (R0)