A New Evaluation Function for Clustering
Renato Tinós, Zhao Liang, Francisco Chicano, Darrell Whitley · 2016
The use of good evaluation functions is essential when evolutionary algorithms are employed for clustering. The NK internal clustering validation measure is proposed for hard partitional clustering. The evaluation function is composed of N subfunctions, where N is the number of objects in the dataset. Each subfunction is influenced by a group of K+1 objects. By using neighbourhood relations among connected small groups, density-based regions can be identified. The NK internal clustering validation measure allows the application of partition crossover (PX). PX for hard partitional clustering is also proposed in this work. By using PX, the evaluation function can be decomposed in q partial evaluations. As a consequence, PX deterministically finds the best of 2q possible offspring at the cost of evaluating 2 solutions. In the experiments, the application of PX resulted in a high number of successful recombinations. It was able to improve partitions defined by the best parents.