Equal-volume quantization of mobile network data using bounding spheres and boxes

Mobile network management systems often utilize quantization algorithms for abstraction and simplification of information, to be later processed by human operators or automated functions. In use cases such as visualization of high dimensional data or processing of anomalous observations, the off- the-shelf algorithms might produce misleading results, without the user realizing that the problem lies in the choice of the applied method. In this paper, we provide a quantization algorithm called Bounding Sphere Quantization (BSQ) that performs better than standard approaches when applied to these use cases, by minimizing the maximum error in the quantization. Since the proposed algorithm is computationally expensive, we also explore an alternative approach, which approximates the results achieved by BSQ while greatly reducing computational complexity. Our evaluation shows that BSQ provides more intuitive results that work better for the selected use cases when compared to the well-known k-Means algorithm.