High Dimensional Data Processing Based on Optimized DPC Algorithm

Abstract

With the rapid growth of science and technology, machine learning and big data analysis have developed more and more difficult. Similarly, data also become difficult to process and classify due to the fact that the data dimension is becoming larger and larger. Furthermore, aiming at the defect due to the amount of data the clustering using speedy examine, search, and discovery of density peaks (also known as DPC) does not adjust to data sets with large dimensions (high-dimensional). Therefore, in this paper, we suggest an optimization procedure, which we pronounce as t-dpc, and is founded on the t-sne dimension lessening technique, and which can also optimize the technique for estimation of the Gaussian kernel function, using unified measurement criteria in solving density. In the simulation based experiments, using two different data sets i.e., the UCI standard data set, and the artificial data set, the proposed DPC procedure is associated with the classical t-dpc algorithm. The empirical evaluation and investigational outcomes illustrate that the proposed method of t-dpc not merely acclimatizes to the high-dimensional data sets, nevertheless it also increases the effectiveness of the classical DPC technique.