An efficient and scalable algorithm for mining maximal

Authors:
Wael Zakaria Abd Allah;Yasser Kotb El Sayed;Fayed Fayek Mohamed Ghaleb
Affiliations:
Faculty of Science, Mathematics/Computer Science Department-Abbassia, Ain Shams University, Cairo, Egypt;Faculty of Science, Mathematics/Computer Science Department-Abbassia, Ain Shams University, Cairo, Egypt and Information Systems Department, College of Computer and Information Sciences, Al-Imam M ...;Faculty of Science, Mathematics/Computer Science Department-Abbassia, Ain Shams University, Cairo, Egypt
Venue:
MLDM'13 Proceedings of the 9th international conference on Machine Learning and Data Mining in Pattern Recognition
Year:
2013

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Abstract

DNA microarrays allow simultaneous measurements of expression levels for a large number of genes within a number of different experimental samples. Mining association rules algorithms are used to reveal biologically relevant associations between different genes under different experimental samples. In this paper, we present a new mining association rules algorithm called Mining Maximal High Confidence Rules (MMHCR). The MMHCR algorithm is based on a column (gene) enumeration method which overcomes both the computational time and memory explosion problems of column-enumeration method used in many of the mining microarray algorithms. MMHCR uses an efficient data structure tree in which each node holds a gene's name and its binary representation. The binary representation is beneficial in two folds. First, it makes MMHCR easily find all maximal high confidence rules. Second, it makes MMHCR more scalable than comparatives. In our experiments on a real microarray dataset, MMHCR attained very promising results and outperformed other counterparts.