As databases continue to grow in size, efficient and effective clustering algorithms play a paramount role in data mining applications. Practical clustering faces several challenges including: identifying clusters of arbitrary shapes, sensitivity to the order of input, dynamic determination of the number of clusters, outlier handling, processing speed of massive data sets, handling higher dimensions, and dependence on usersupplied parameters. Many studies have addressed one or more of these challenges. PYRAMID, or parallel hybrid clustering using genetic programming and multiobjective fitness with density, is an algorithm that we introduced in a previous research, which addresses some of the above challenges. While leaving significant challenges for future work, such as handling higher dimensions, PYRAMID employs a combination of data parallelism, a form of genetic programming, and a multiobjective density-based fitness function in the context of clustering. This study adds to our previous research by exploring the detection capability of PYRAMID against a challenging dataset and evaluating its independence on user supplied parameters.