A new similarity measure for microarray data analysis

A number of clustering algorithms have been used for microarray data analysis. However, the performance of these methods is significantly degraded due to the presence of noise. In this paper, we introduce a robust clustering algorithm based on a new similarity measure. The key concept of the new similarity measure is to measure the similarity between two data points by their sub-dimensions. For example, assume that x₁, x₂ and x₃ are 10 dimensional data vectors. The data point X3 is said to be closer to x₁ than x₂ if more than half of the dimensions of x₁ and x₃ are closer to x₁ than X₂. Thus, if two patterns are very similar except a small amount of features or noise, this measure will preserve the similarity. Experimental results show that the clustering algorithm using this measure produces better results than commonly used similarity measures.