Selecting interesting zones at Aburr\'a valley and St. Nicholas Valley's using the identification method of Density-based Clustering and Improved Nearest Neighbor applied on social networks

TL;DR

This paper presents a method combining density-based clustering and an improved nearest neighbor algorithm to identify interesting zones in social network data, aiding demographic analysis and automated application development.

Contribution

It introduces a novel approach integrating density-based clustering with an improved nearest neighbor method for analyzing social network geospatial data.

Findings

Effective identification of interest zones in social network data

Enhanced demographic segmentation accuracy

Potential for automated analysis applications

Abstract

More than ever, social networks have become an important place in the interaction and behaviour of humans in the last decade. This valuable position makes it imperative to analyze different aspects of everyday life and science in general. This paper illustrates the process of capturing and storing information, the application of density-based clustering and improved nearest neighbor, and a review of the results. The study also shows the elements used in the identification of areas of interest through clusters, circumferences and coverage radii obtained for a demographic segmentation analysis of the information procured from Twitter, Flickr and the like. This results in more profound conclusions about a predefined topic or theme. Finally, the need arises to develop an application that makes all the defined process automatic, allowing final users interested in those topics to have access to it and get important results for their organizations or interest.