Towards an Adaptive Encoding for Evolutionary Data Clustering

A key consideration in developing optimization approaches for data clustering is choice of a suitable encoding. Existing encodings strike different trade-offs between model and search complexity, limiting the applicability to data sets with particular properties or to problems of moderate size. Recent research has introduced an additional hyperparameter to directly govern the encoding granularity in the multi-objective clustering algorithm MOCK. Here, we investigate adapting this important hyperparameter during run-time. In particular, we consider a number of different trigger mechanisms to control the timing of changes to this hyperparameter and strategies to rapidly explore the newly "opened" search space resulting from this change. Experimental results illustrate distinct performance differences between the approaches tested, which can be explained in light of the relative importance of initialization, crossover and mutation in MOCK. The most successful strategies meet the clustering performance achieved for an optimal (a priori) setting of the hyperparameter, at a ~40% reduction of computational expense.