خوشه‌بندی خودکار داده‌های مختلط با استفاده از الگوریتم ژنتیک

In the real world clustering problems, it is often encountered to perform cluster analysis on data sets with mixed numeric and categorical values. However, most existing clustering algorithms are only efficient for the numeric data rather than the mixed data set. In addition, traditional methods, for example, the K-means algorithm, usually ask the user to provide the number of clusters. In this paper, we propose a new method to cluster mixed data and automatically evolve the number of clusters as well as clustering of data set. In the proposed method, Davies-Bouldin Index is used as fitness function and we use the genetic algorithm to optimize fitness function. Also, we use a more accurate distance measure for calculating the distance between categorical values. The performance of this algorithm has been studied on real world and simulated data sets. Comparisons with other clustering algorithms illustrate the effectiveness of this approach.