A geometric density-based sample reduction method

Analysis of network traffic, financial transactions, and mobile communications are examples of applications where examining entire samples of a large dataset is computationally expensive, and requires significant memory space. A common approach to address this challenge is to reduce the number of samples without compromising the accuracy of analyzing them. In this paper, we propose a new cluster-based sample reduction method which is unsupervised, geometric, and density-based. The original data is initially divided into clusters, and each cluster is divided into "portions" defined as the areas between two concentric circles. Then, using the proposed geometric-based formulas, the selection value of each sample belonging to a specific portion is calculated. Samples are then selected from the original data according to the corresponding calculated selection value. The performance of the proposed method is measured on various datasets and compared with several cluster-based and density-based methods. We conduct various experiments on the NSL-KDD, KDDCup99, and IUSTsip datasets, and evaluate the performance of the proposed method by measuring the cluster validity indices, as well as the accuracy of the classifier applied on the reduced data. We demonstrate that the reduced dataset has similar sample scattering as that of the original dataset. We also demonstrate that, while reducing the sample size of the input dataset in half, the classification accuracy is not reduced significantly, indicating that the proposed method selects the most relevant samples from the original dataset.