Abstract
In recent years interest has grown in “mining” spatial gene expression databases to extract novel and interesting information. Knowledge Discovery in Databases (KDD) has been recognized as an emerging research area. Association rules discovery is an important KDD technique for better data understanding. This paper proposes an enhancement with a fast and memory efficient algorithm to mine association rules from spatial gene expression data. In this paper, the features of Similarity matrix, Boolean matrix and Bit operations are combined to generate frequent gene expression patterns without candidate generation and association rules with fixed antecedent and multiple consequents using Bit operations has been generated. The obtained results accurately reflected knowledge hidden in the datasets under examination.
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Anandhavalli, M., Ghose, M.K., Gauthaman, K. (2010). Mining Frequent and Associated Gene Expression Patterns from Spatial Gene Expression Data: A Proposed Approach. In: Ranka, S., et al. Contemporary Computing. IC3 2010. Communications in Computer and Information Science, vol 94. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14834-7_12
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DOI: https://doi.org/10.1007/978-3-642-14834-7_12
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