Fixed-Resolution Growing Neural Gas for Clustering the Mobile Networks Data

Abstract

An important property of the competitive neural models for data clustering is autonomous discovery of the data structure without a need of a priori knowledge. Growing Neural Gas (GNG) is one of the commonly used incremental clustering models that aims at preserving the topology and the distribution of the input data. Keeping the data distribution unchanged has already been recognized as a problem leading to bias sampling of input data. This is undesired for the use cases such as mobile network management and troubleshooting where is important to capture all the relevant network states uniformly. In this paper we propose a novel incremental clustering approach called Fixed Resolution GNG (FRGNG) that keeps the input data representation at the fixed resolution avoiding the oversampling and undersampling problems of original GNG algorithm. Furthermore, FRGNG introduces a native stopping criteria by terminating the run once the input data is represented with the desired fixed resolution. Additionally, the FRGNG has a potential of the algorithm acceleration which is especially important when large input data set is applied. We apply the FRGNG model to analyze the mobile network performance data and evaluate its benefits compared to GNG approach.