Impact of Cluster Validity Measures on Performance of Hybrid Models Based on K-means and Decision Trees

Building predictive models in customer relationship management refers to each stage in the customer’s lifecycle, i.e. the customer acquisition, development and retention. One may notice that the construction of predictive models is more and more frequently accompanied by an attempt to combine analytical tools of the same type or combining various methods. The first approach is named “ensemble methods”, while the second can be referred to as a “hybrid” one. Authors decided to combine k-means algorithm with decision trees and examine whether cluster validity measures influence the performance of a model. During experiments 5 different cluster validity indices and 8 datasets were used. The performance of models was evaluated by using popular measures such as: accuracy, precision, recall, G-mean, F-measure and lift in the first and in the second decile. The results are far from causing enthusiasm, however, they are promising in some fields.