  Finding neighbourhood structures is very useful in extracting valuable
relationships among data samples. This paper presents a survey of recent
neighbourhood construction algorithms for pattern clustering and classifying
data points. Extracting neighbourhoods and connections among the points is
extremely useful for clustering and classifying the data. Many applications
such as detecting social network communities, bundling related edges, and
solving location and routing problems all indicate the usefulness of this
problem. Finding data point neighbourhood in data mining and pattern
recognition should generally improve knowledge extraction from databases.
Several algorithms of data point neighbourhood construction have been proposed
to analyse the data in this sense. They will be described and discussed from
different aspects in this paper. Finally, the future challenges concerning the
title of the present paper will be outlined.
