Towards the expansion to the upcoming cities: a clustering approach for luggit

Abstract

Acknowledging the success of clustering techniques as decision support tools, this paper proposes the development of an enhanced K-means algorithm to resolve LUGGit’s problem of expansion. With the intent of identifying the cities that most accurately meet the company’s expectations, an extensive process of data collection, reflecting a wide-ranging market-study, was on the basis of the creation of the “Weighted K-means”, a clustering method capable of weighting the various attributes based on their relative significance to each member of the team, being adjustable to the present and the future needs of the company.