
Costa, K. A. P., Pereira, L. A. M., Nakamura, R. Y. M., Pereira, C. R., Papa, J. P., & Xavier Falcão, A. (2015). A natureinspired approach to speed up optimumpath forest clustering and its application to intrusion detection in computer networks. Innovative Applications of Artificial Neural Networks in Engineering, 294, 95–108.
Abstract: Abstract We propose a natureinspired approach to estimate the probability density function (pdf) used for data clustering based on the optimumpath forest algorithm (OPFC). OPFC interprets a dataset as a graph, whose nodes are the samples and each sample is connected to its knearest neighbors in a given feature space (a knn graph). The nodes of the graph are weighted by their pdf values and the pdf is computed based on the distances between the samples and their knearest neighbors. Once the knn graph is defined, OPFC finds one sample (root) at each maximum of the pdf and propagates one optimumpath tree (cluster) from each root to the remaining samples of its dome. Clustering effectiveness will depend on the pdf estimation, and the proposed approach efficiently computes the best value of k for a given application. We validate our approach in the context of intrusion detection in computer networks. First, we compare OPFC with data clustering based on kmeans, and selforganization maps. Second, we evaluate several metaheuristic techniques to find the best value of k.

