Modeling uncertain data using Monte Carlo integration method for clustering

Abstract

Nowadays, data clustering is an important task to the mining research community since the availability of uncertain data is increasing rapidly in many applications such as weather forecasting, business information management systems. In this work, proposed Monte Carlo integration based uncertain objects modeling technique is compared with three state-of-the-art methods namely, kernel density estimation, Dempster–Shafer, and Monte Carlo simulation. Then Kullback–Leibler and Jeffrey divergences are used to measure the similarity between uncertain objects and merge them with modified DBSCAN and k-medoids clustering algorithms. A heuristic algorithm is proposed to find the optimum radius, which is one of the inputs of DBSCAN. All the experiments are performed on one synthesized dataset and three real datasets namely, weather data, Japanese vowels and activity of daily living data. Five performance measures namely, accuracy, precision, recall, F-score, and Jaccard index are considered for comparing proposed method with state-of-the-art methods. Two non-parametric tests namely, Wilcoxon rank sum and sign test are also conducted. These results denote the effectiveness and efficiency of the proposed method over state-of-the-art methods.