Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICDE.2009.80
Title: Finding time-lagged 3D clusters
Authors: Xu, X. 
Lu, Y.
Tan, K.-L. 
Tung, A.K.H. 
Issue Date: 2009
Source: Xu, X.,Lu, Y.,Tan, K.-L.,Tung, A.K.H. (2009). Finding time-lagged 3D clusters. Proceedings - International Conference on Data Engineering : 445-456. ScholarBank@NUS Repository. https://doi.org/10.1109/ICDE.2009.80
Abstract: Existing 3D clustering algorithms on gene × sample × time expression data do not consider the time lags between correlated gene expression patterns. Besides, they either ignore the correlation on time subseries, or disregard the continuity of the time series, or only validate pure shifting or pure scaling coherent patterns instead of the general shiftingand- scaling patterns. In this paper, we propose a novel 3D cluster model, S 2D 3 Cluster, to address these problems, where S 2 reflects the shifting-and-scaling correlation and D 3 the 3- Dimensional gene×sample×time data.Within the S2D3 Cluster model, expression levels of genes are shifting-and-scaling coherent in both sample subspace and time subseries with arbitrary time lags. We develop a 3D clustering algorithm, LagMiner, for identifying interesting S 2D 3 Clusters that satisfy the constraints of regulation (γ), coherence (ε), minimum gene number (MinG), minimum sample subspace size (MinS) and minimum time periods length (MinT). Experimental results on both synthetic and real-life datasets show that LagMiner is effective, scalable and parameter-robust. While we use gene expression data in this paper, our model and algorithm can be applied on any other data where both spatial and temporal coherence are pursued. © 2009 IEEE.
Source Title: Proceedings - International Conference on Data Engineering
URI: http://scholarbank.nus.edu.sg/handle/10635/43325
ISBN: 9780769535456
ISSN: 10844627
DOI: 10.1109/ICDE.2009.80
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