Alignment versus variation methods for clustering microarray time-series data

In the past few years, it has been shown that traditional clustering methods do not necessarily perform well on time-series data because of the temporal relationships involved in such data - this makes it a particularly difficult problem. In this paper, we compare two clustering methods that have be...

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Bibliographic Details
Published in:IEEE Congress on Evolutionary Computation pp. 1 - 8
Main Authors: Subhani, Numanul, Yifeng Li, Ngom, Alioune, Rueda, Luis
Format: Conference Proceeding
Language:English
Published: IEEE 01.07.2010
Subjects:
Clustering algorithms
Clustering methods
Gene expression
Indexes
Interpolation
Prototypes
Spline
ISBN:1424469090, 9781424469093
ISSN:1089-778X
Online Access:Get full text
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Summary:In the past few years, it has been shown that traditional clustering methods do not necessarily perform well on time-series data because of the temporal relationships involved in such data - this makes it a particularly difficult problem. In this paper, we compare two clustering methods that have been introduced recently, especially for gene expression time-series data, namely, multiple-alignment (MA) clustering and variation-based co-expression detection (VCD) clustering approaches. Both approaches are based on a transformation of the data that takes into account the temporal relationships, and have been shown to effectively detect groups of co-expressed genes. We investigate the performances of the MA and VCD approaches on two microarray time-series data sets and discuss their strengths and weaknesses. Our experiments show the superior accuracy of MA over VCD when finding groups of co-expressed genes.
ISBN:1424469090
9781424469093
ISSN:1089-778X
DOI:10.1109/CEC.2010.5586111