Clustering is an unsupervised analytical technique for processing data that works by grouping elements of a set in order to form clusters of similar items. This task lies at the base level of many other tasks including machine vision and artificial intelligence. Big data sets present challenges for clustering due to the size and complexity of the data to be processed. Previous work in this domain resulted in an algorithm called Fast Density-Grid Clustering, which is designed to create a grid structure on the data and then merging cells based on local density. In this paper, we focus on an ongoing modification to this algorithm that could also be used for other density-based clustering algorithms, based on adaptive grids that have an irregular spacing. Testing shows that while the initial results for the first stage of implementation resulted in an average 29.728% loss in accuracy with no significant speed increase, there is a lot of further room for experimentation and development for this approach.