A novel method to find appropriate for ε for DBSCAN
ACIIDS'10 Proceedings of the Second international conference on Intelligent information and database systems: Part I
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Clustering is the problem of partitioning a (large) set of data using unsupervised techniques.Today, there exist many clustering techniques. The most important characteristic of a clustering technique is the shape of the cluster it can find. In this paper, we propose a method that is capable to find arbitrary shaped clusters and uses simple geometric constructs, Circlusters. Circlusters are different radius sectored circles. Circlusters can be used to create many hybrid approaches in mixture with density based or partitioning based methods. We also proposed two new clustering methods that are capable to find complex clusters in $O(n)$, where $n$ is the size of the data set. Both of the methods are two phase. In the first phase, circlusters are mined to approximate the shape of the data set. In the second phase, connected circlusters are found to mine the final clusters using different approaches.