A New Encoding Scheme for a Bee-Inspired Optimal Data Clustering Algorithm

The amount of data generated in different knowledge areas has made necessary the use of data mining tools capable of automatically analyzing and extracting knowledge from datasets. Clustering is one of the most important tasks in data mining and can be defined as the process of partitioning objects into groups or clusters, such that objects in the same group are more similar to one another than to objects belonging to other groups. In this context, this paper aims to propose a new encoding scheme to COpt Bees, a bee-inspired algorithm to solve data clustering problems. In this new encoding, each bee represents a prototype for the clusters. The algorithm was run for different datasets and the results obtained showed high quality clusters and diversity of solutions, whilst a suitable number of clusters was automatically determined.