A novel hierarchical clustering approach based on data gravitation model

Abstract

The purpose of clustering is to group the data points into several clusters according to their similarity. Inspired by law of gravitation, each data point considered as an object is exposed in data space and influenced by the data gravitation of the other data points. In this paper, a novel hierarchical clustering approach (NGHC) is proposed based on local data gravitation. Firstly, the dataset is partitioned into some clusters as intermediate result by a simple gravity-based clustering approach. Then the intermediate clusters are merged by a new linkage measure which combines the data gravitations between two clusters until the satisfactory result is obtained. Two real-world datasets are used to validate the clustering performance of NGHC compared with five representative clustering algorithms. The results exhibit that the NGHC algorithm obtains the best performance on low- and high-dimensional datasets.