Accepted article at ADAC (Advances in Data Analysis and Classification)

Advances in Data Analysis and Classification. 2022

Authors

Theresa Ullmann, Anna Beer, Maximilian Hünemörder, Thomas Seidl, Anne-Laure Boulesteix

Abstract

When researchers publish new cluster algorithms, they usually demonstrate the strengths of their novel approaches by comparing the algorithms' performance with existing competitors. However, such studies are likely to be optimistically biased towards the new algorithms, as the authors have a vested interest in presenting their method as favorably as possible in order to increase their chances of getting published. Therefore, the superior performance of newly introduced cluster algorithms is over-optimistic and might not be confirmed in independent benchmark studies performed by neutral and unbiased authors. This problem is known among many researchers, but so far, the different mechanisms leading to over-optimism in cluster algorithm evaluation have never been systematically studied and discussed. Researchers are thus often not aware of the full extent of the problem. We present an illustrative study to illuminate the mechanisms by which authors - consciously or unconsciously - paint their cluster algorithm's performance in an over-optimistic light. Using the recently published cluster algorithm Rock as an example, we demonstrate how optimization of the used data sets or data characteristics, of the algorithm's parameters and of the choice of the competing cluster algorithms leads to Rock's performance appearing better than it actually is. Our study is thus a cautionary tale that illustrates how easy it can be for researchers to claim apparent "superiority" of a new cluster algorithm. This illuminates the vital importance of strategies for avoiding the problems of over-optimism (such as, e.g., neutral benchmark studies), which we also discuss in the article.