In this article, the author studies the distances and similarities between key performance measures based on clustering data-mining techniques. Using 27 performance measures related to 211 investment funds and calculated for three subperiods (six months, one year, and three years), these clustering techniques enable the observation of the three main dimensions of performance formed by the Sharpe ratio, the information ratio, and beta coefficients. Once more, the Sharpe ratio appears to be the key element for traditional performance measures. Thereby, each obtained cluster can be used to complete and improve classical fund performance analysis. From a practical perspective, these results confirm not only the relevance of a selective approach in choosing measures but also the efficiency of an integrated approach to performance analysis and the selection of funds. To this end, the author finds in an ex ante perspective that ranking based on a combination of Jensen’s alpha, the Sharpe ratio, and absolute performance over a three-year span dominates clearly all other rankings.