—Multistage stochastic programs are effective for solving long-term planning problems under uncertainty. Such programs are usually based on scenario generation model about future environment developments. In the present paper, the scenario model is developed for the case when enough data paths can be generated, but due to solvability of stochastic program the scenario tree has to be constructed. The proposed strategy is to generate multistage scenario tree from the set of individual scenarios by bundling scenarios based on cluster analysis. The K-means clustering approach is modified to capture the interstage dependencies. Such generation of scenario tree can be useful in cases when it is difficult to construct the adequate scenario tree from the stochastic differential equations or time-series models, and the sampled paths can be obtained by sampling or resampling techniques. While generating the initial fan of individual scenarios, the copula is employed for modeling the dependence...