Clustering is an important and challenging task in data mining. As a kind of generalized density-based clustering methods, DENCLUE algorithm has many remarkable properties, but the quality of clustering results strongly depends on the adequate choice of two parameters: density parameter σ and noise threshold ξ. In this paper, by investigating the influence of the two parameters of DENCLUE algorithm on the clustering results, we firstly show that an optimal σ should be chosen to obtain good clustering results. Then, an entropy-based method is proposed for the optimal choice of σ. Further, noise threshold ξ is estimated to produce a reasonable pattern of clustering. Finally, experiments are performed to illustrate the effectiveness of our methods.