A new method for detection of clustering based on four zones Apollonius circle

摘要

Abstract In many fields of machine learning such as classifying and clustering, neighborhood construction algorithms are used to model local relationships between data samples and to build global structure from local information. In finding connection among the data points, neighborhood is undeniably useful for data processing. Therefore, a very major issue is to find a novel approach to locating neighborhood among data points. If the geometric relationships existing between the data points in the neighborhood area are accurately explored, it will be feasible to observe the behavioral rules as well as the similarities among the data. This will also help identify indirect and direct neighborhood ranges. This study aims to find neighborhood accurately by means of the Apollonius circle zones. The experimental validation against well-known k-nearest neighbor and ε-neighborhood is also an indication of the robustness of the method in real data sets.