The Choice of Metrics for Clustering Algorithms

Methods of data analysis and automatic processing are treated as knowledge discovery. In many cases it is necessary to classify data in some way or find regularities in the data. That is why the notion of similarity is becoming more and more important in the context of intelligent data processing systems. It is frequently required to ascertain how the data are interrelated, how various data differ or agree with each other, and what the measure of their comparison is. An important part in detection of similarity in clustering algorithms plays the accuracy in the choice of metrics and the correctness of the clustering algorithms operation.