An Efficient and Scalable Algorithm for Mining Maximal

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• 作者：Wael Zakaria Abd Allah (20)
  Yasser Kotb El Sayed (20) (21)
  Fayed Fayek Mohamed Ghaleb (20)
  
• 关键词：Data mining ; DNA microarray ; mining association rules ; closed itemsets ; row enumeration ; column enumeration ; maximal high confidence rules
• 刊名：Lecture Notes in Computer Science
• 出版年：2013
• 出版时间：2013
• 年：2013
• 卷：7988
• 期：1
• 页码：367-378
• 全文大小：621KB
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• 作者单位：Wael Zakaria Abd Allah (20)
  Yasser Kotb El Sayed (20) (21)
  Fayed Fayek Mohamed Ghaleb (20)
  
  20. Faculty of Science, Mathematics/Computer Science Department-Abbassia, Ain Shams University, Cairo, Egypt
  21. Information Systems Department, College of Computer and Information Sciences, Al-Imam Muhammad ibn Saud Islamic University, Riyadh, KSA
  

文摘

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.