We propose a new data-driven procedure for optimal selection of tuning parameters in dynamic clustering algorithms, using the notion of stability probe. Due to the shape of the stability probe dynamics, we refer to the new clustering stability procedure as Downhill Riding (DR). We study final sample performance of DR in conjunction with DBSCAN and TRUST in application to clustering synthetic times series and yearly temperature records in Central Germany.