Validating clustering for gene expression data Webcam sex pretoria

Appropriate data mining exploration methods can reveal valuable but hidden information in today’s large quantities of transactional data.While association rules generation is commonly used for transactional data analysis, clustering is rather rarely used for analysis of this type of data.The proposed distance measure can be used for measuring the similarity between different ARIMA models as well.

a single large observation can yield a bad estimate).Supposedly there is an advantage to using the pairwise distance measure in the k-medoid algorithm, instead of the more familiar sum of squared Euclidean distance-type metric to evaluate variance that we find with k-means.And apparently this different distance metric somehow reduces noise and outliers.If we don't consider the object exchanges among the clusters, with k-means you will get the center of cluster (21.2,21.2) which is pretty distracted by the point (100,100). However, with k-medoid will choose the center among (1,1),(1,2),(2,1),and (2,2) according to its algorithm.

If you look up literature on the median, you will see plenty of explanations and examples why the median is more robust to outliers than the arithmetic mean. Which do you think is more representative of the data set?