IMCKDE algorithm: an improvement in a clustering technique based on kernel density estimation

Given the increasing volume of data available, much of which lacks established categories, the development of algorithms capable of finding patterns in raw, unclassified data is becoming increasingly important. One type of clustering algorithm is the MulticlusterKDE, which is based on the search for centroids by maximizing the kernel density estimation function, which assumes local maxima at points of highest data density. The aim of this work is to propose a clustering algorithm based on improvements to the MulticlusterKDE algorithm, named IMCKDE. These improvements occur both in terms of response quality and computational time. Furthermore, it was observed that the MulticlusterKDE algorithm has prohibitively long computation times for large datasets, highlighting the relevance of IMCKDE.