Abstract
The efficiency of resources is the footstone for building prognostics and health management system or safety system. In this study, we proposed an efficient bicluster mining algorithm: CeCluster algorithm, which mines trend bicluster in real-valued resource effectiveness matrices. To improve the mining efficiency, CeCluster algorithm mines maximal trend bicluster using the method of column extension and multiple pruning strategies without candidate maintenance. CeCluster algorithm can not only mine resource patterns with effectiveness in the downtrend, but also mine those with effectiveness in the uptrend. CeCluster algorithm can also mine resource patterns without change of effectiveness. The experimental result shows our algorithm is efficient than traditional algorithm.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Pecht M et al (2010) A prognostics and health management roadmap for information and electronics-rich systems. Microelectron Reliab 50:317–323
Cheng Y, Church GM (2000) Biclustering of expression data. In: Proceedings of the 8th international conference intelligent systems for molecular biology (ISMB00). ACM Press, pp 93–103
Ben A et al (2003) Discovering local structure in gene expression data: the order-preserving submatrix problem. J Comput Biol 10:373–384
Cheng KO et al (2007) Bivisu: software tool for bicluster detection and visualization. Bioinformatics 23:2342–2344
Zhao L, Zaki MJ (2005) MicroCluster: an efficient deterministic biclustering algorithm for microarray data. IEEE Intell Syst 20(6):40–49 (special issue on Data Mining for Bioinformatics)
Wang M, Shang X, Zhang S, Li Z (2010) FDCluster: mining frequent closed discriminative bicluster without candidate maintenance in multiple microarray datasets. In: ICDM 2010 workshop on biological data mining and its applications in healthcare, p 779–786
Wang M, Shang X et al (2013) Efficient mining differential co-expression biclusters in microarray datasets. Gene 518:59–69
Yang M, Shang X et al (2013) Bicluster algorithm facing the time-series gene expression data. Appl Res Comput 30(8):2308–2314
Acknowledgments
This study is supported by Avionics Science Foundation (No. 20125552053), National Key Basic Research Program of China (No. 2014CB744900), and Graduate starting seed fund of Northwestern Polytechnical University (No. Z2013130).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhang, L., Wang, M., Gu, Q., Zhai, Z., Wang, G. (2014). Efficient Mining Maximal Trend Biclusters in Real-Valued Resource Effectiveness Matrix: The CeCluster Algorithm. In: Wang, J. (eds) Proceedings of the First Symposium on Aviation Maintenance and Management-Volume II. Lecture Notes in Electrical Engineering, vol 297. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54233-6_5
Download citation
DOI: https://doi.org/10.1007/978-3-642-54233-6_5
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-54232-9
Online ISBN: 978-3-642-54233-6
eBook Packages: EngineeringEngineering (R0)