Robust Density-Based Data Clustering Using a Quantum-Inspired Genetic Algorithm

Density-based clustering methods such as DBSCAN are known to be robust against outliers in data; however, they are sensitive to user-specified parameters, the selection of which are not trivial. In this paper, the user-defined parameters of DBSCAN are evolved using a quantum-inspired genetic algorithm (QGA). The quantum-bit or Q-bit representation of a partition is an improvement over the more popular binary label-based representations and real-coded representation of partition cluster centers. A resulting algorithm called DBSCAN-QGA in the relational data space is proposed, and three different fitness functions are devised to evaluate partitions both in terms of cluster compactness and separation, and the relative number of entities classified as noise. The performance of the proposed algorithm is compared to synthetic and benchmark datasets from the UCI machine learning repository with encouraging results.