A parallel algorithm for multilevel graph partitioning and sparse matrix ordering
Journal of Parallel and Distributed Computing
A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs
SIAM Journal on Scientific Computing
Visualization Techniques for Mining Large Databases: A Comparison
IEEE Transactions on Knowledge and Data Engineering
Relationship-Based Clustering and Visualization for High-Dimensional Data Mining
INFORMS Journal on Computing
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In this study, we propose a modified version of relationship based clustering framework dealing with density based clustering and outlier detection in high dimensional datasets. Originally, relationship based clustering framework is based on METIS. Therefore, it has some drawbacks such as no outlier detection and difficulty of determining the number of clusters. We propose two improvements over the framework. First, we introduce a new space which consists of tiny partitions created by METIS, hence we call it micro-partition space. Second, we used DBSCAN for clustering micro-partition space. The visualization of the results are carried out by CLUSION. Our experiments have shown that, our proposed framework produces promising results on high dimensional datasets.