Abstract
Unlike the traditional datasets, gene expression datasets typically contain a huge number of items and few transactions. Though there were a large number of algorithms that had been developed for mining frequent closed patterns, their running time increased exponentially with the average length of the transactions increasing. Therefore, most current methods for high-dimensional gene expression datasets were impractical. In this paper, we proposed a new data structure, tidset-prefix-plus tree (TP+-tree), to store the compressed transposed table of dataset. Based on TP+-tree, an algorithm, TP+close, was developed for mining frequent closed patterns in gene expression datasets. TP+close adopted top-down and divide-and-conquer search strategies on the transaction space. Moreover, TP+close combined efficient pruning and effective optimizing methods. Several experiments on real-life gene expression datasets showed that TP+close was faster than RERII and CARPENTER, two existing algorithms.
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Miao, Y., Chen, G., Song, B., Wang, Z. (2006). TP+Close: Mining Frequent Closed Patterns in Gene Expression Datasets. In: Dalkilic, M.M., Kim, S., Yang, J. (eds) Data Mining and Bioinformatics. VDMB 2006. Lecture Notes in Computer Science(), vol 4316. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11960669_11
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DOI: https://doi.org/10.1007/11960669_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-68970-6
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