UNSUPERVISED CLUSTERING USING FRACTAL DIMENSION
Clustering can be defined as the process of "grouping" a collection of objects into subsets or clusters. The clustering problem has been addressed in numerous contexts and by researchers in different disciplines. This reflects its broad appeal and usefulness as an exploratory data analysis approach. Unsupervised clustering algorithms have been developed to address real world problems in which the number of clusters present in the dataset is unknown. These algorithms approximate the number of clusters while performing the clustering procedure. This paper is a first step towards the development of unsupervised clustering algorithms capable of identifying clusters within clusters. To this end, an unsupervised clustering algorithm is modified so as to take into consideration the fractal dimension of the data. The experimental results indicate that this approach can provide further qualitative information compared to the unsupervised clustering algorithm.