Tree-Based Hybrid Genetic Algorithm for Density-Based Data Clustering

Data clustering algorithms partition a given set of data points into groups containing very similar data points. Representative-based and density-based algorithms are generally used for data clustering. These algorithms are heuristic algorithms and may stuck at a sub-optimal clustering. Crisp clustering problem is a combinatorial optimization problem. Genetic Algorithms generally perform better than heuristic algorithms for combinatorial optimization. In this work, we propose a hybrid Genetic Algorithm for density-based clustering. For this purpose, we represent a cluster using a forest of trees, where the nodes of the trees are the data points. We use a tree-based fitness function. Beside 1-point crossover, we use a deterministic improvement of offspring. We implement the proposed algorithm using C language and run on a personal computer. We experiment with five datasets from UCI Machine Learning Repository. The proposed algorithm outperforms for both low and high-dimensional datasets over existing algorithms, except for one high-dimensional dataset.