The neural gas has been successfully used for prototype based clustering approaches. Its topology based quantization effectively aids in approximate spectral clustering (ASC) to define distinct similarity criteria which are optimally selected for the relevant application. To utilize the advantages of ASC by harnessing those criteria derived from different information types, we propose a neural gas based approximate spectral clustering ensemble (NGASCE). The NGASCE obtains a joint decision for accurate partitioning, by a 2-step ensemble approach derived from 1-step graph-based models. We show the outperformance of NGASCE on five datasets from UCI Machine Learning Repository.