A survey of neighborhood construction algorithms for clustering and classifying data points.

Clustering and classifying are overriding techniques in machine learning. Neighborhood construction as a key step in these techniques has been extensively used for modeling local relationships between data samples, and constructing global structures from local information. The goal of the neighborhood construction process is to improve the quality of individual data point categorizing. Many applications such as detecting social network communities, bundling related edges, solving location, and routing problems all indicate the importance of this problem. This paper presents theoretical and practical studies of state-of-the-art methods in the context of neighborhood construction which is resulted in a coherent and comprehensive survey to analyze these methods. To this end, significant algorithms of neighborhood construction have been proposed to analyze data points which are very useful for the community of clustering and classifying practitioners since showing the advantages and disadvantages of each algorithm. All of them will be described and discussed deeply in different aspects, which help to select an appropriate solution for problems. A taxonomy of these algorithms is presented and their differences and some important applications are explained. Finally, the future challenges concerning the title of the present paper are outlined. [ABSTRACT FROM AUTHOR]