Many spectrum-based fault localization techniques have been proposed to measure how likely each program element is the root cause of a program failure. For various bugs, the best technique to localize the bugs may differ due to the characteristics of the buggy programs and their program spectra. In this paper, we leverage the diversity of existing spectrum-based fault localization techniques to better localize bugs using data fusion methods. Our proposed approach consists of three steps: score normalization, technique selection, and data fusion. We investigate two score normalization methods, two technique selection methods, and five data fusion methods resulting in twenty variants of Fusion Localizer. Our approach is bug specific in which the set of techniques to be fused are adaptively selected for each buggy program based on its spectra. Also, it requires no training data, i.e., execution traces of the past buggy programs. We evaluate our approach on a common benchmark dataset and a dataset consisting of real bugs from three medium to large programs. Our evaluation demonstrates that our approach can significantly improve the effectiveness of existing state-of-the-art fault localization techniques. Compared to these state-of-the-art techniques, the best variants of Fusion Localizer can statistically significantly reduce the amount of code to be inspected to find all bugs. Our best variants can increase the proportion of bugs localized when developers only inspect the top 10% most suspicious program elements by more than 10% and increase the number of bugs that can be successfully localized when developers only inspect up to 10 program blocks by more than 20%.