2018/09/27 by Shahin Pourbahrami, Pourbahrami, Shahin, Leyli Mohammad Khanli +3
Computer Science · #Computational Geometry (cs.CG) #FOS: Computer and information sciences #Face and Expression Recognition
paper · pdf · doi:10.48550/arxiv.1809.10702
openalex publication_date 2018/09/27 · openalex created_date 2022/08/02 · openalex updated_date 2026/07/28
Neighborhood construction models are important in finding connection among\nthe data points, which helps demonstrate interrelations among the information.\nHence, employing a new approach to find neighborhood among the data points is a\nchallenging issue. The methods, suggested so far, are not useful for\nsimultaneous analysis of distances and precise examination of the geometric\nposition of the data as well as their geometric relationships. Moreover, most\nof the suggested algorithms depend on regulating parameters including number of\nneighborhoods and limitations in fixed regions. The purpose of the proposed\nalgorithm is to detect and offer an applied geometric pattern among the data\nthrough data mining. Precise geometric patterns are examined according to the\nrelationships among the data in neighborhood space. These patterns can reveal\nthe behavioural discipline and similarity across the data. It is assumed that\nthere is no prior information about the data sets at hand. The aim of the\npresent research study is to locate the precise neighborhood using Apollonius\ncircle, which can help us identify the neighborhood state of data points. High\nefficiency of Apollonius structure in assessing local similarities among the\nobservations has opened a new field of the science of geometry in data mining.\nIn order to assess the proposed algorithm, its precision is compared with the\nstate-of-the-art and well-known (k-Nearest Neighbor and epsilon-neighborhood)\nalgorithms.\n