Variable density based clustering
The class of density based clustering algorithms excels in detecting clusters of arbitrary shape. DBSCAN, the most common representative, has been demonstrated to be useful in a lot of applications. Still the algorithm suffers from two drawbacks, namely a non trivial parameter estimation for a given dataset and the limitation to data sets with constant cluster density. The first was already addressed in our previous work, where we presented two hierarchical implementations of DBSCAN. In combination with a simple optimization procedure, those proofed to be useful in detecting appropriate parameter estimates based on an objective function. However, our algorithm was not capable of producing clusters of differing density. In this work we will use the hierarchical information to extract variable density clusters and nested cluster structures. Our evaluation shows that the clustering approach based on edge lengths of the dendrogram or based on area estimates successfully detects clusters of arbitrary shape and density.
2016 IEEE Symposium Series on Computational Intelligence (SSCI)