A Two-staged ClusteringAlgorithm for Multiple Scales

Cluster analysis is a data mining technique used to identify hidden patterns within data. Most clustering algorithms treat different fields of data with equal weights and calculate the “distance” using the same method. They ignore the fact that different fields of data have different scales; therefore, the “distance” should be calculated differently. This study incorporated a traditional clustering algorithm with expert subjective judgment, and used different methods to calculate the degree of similarity for four different scales -nominal, ordinal, interval and ratio. This study proposes a two-staged clustering algorithm to improve the process. In the first stage, training data was used to determine the parameters that improved clustering quality. In the second stage, different methods were used to calculate the degree of similarity for four different scales of data and treated different fields with unequal weights. To evaluate the outcomes of this proposed clustering method, four standard data sets were used for testing. They were the Wisconsin Breast Cancer Data, Contraceptive Method Choice Data, Iris Education Data, and Balance Scale Weight & Distance Data. The results were positive; the algorithm using multi-scales resulted in a better quality clustering. Also, the algorithm incorporating expert subjective weighting had better accuracy in clustering.