A Minimum Spanning Tree Algorithm Based on Fuzzy Weighted Distance

Abstract

The traditional minimum spanning tree clustering algorithm uses a simple Euclidean distance metric method to calculate the distance between two entities. For the processing of noise data, the similarity can’t be well described. In this regard, first of all, we integrate fuzzy set theory to improve, and propose a method with indeterminacy fuzzy distance measurement. In the distance metric method, fuzzy set theory is introduced to measure the differences between two entities. Moreover, the attributes are fuzzy weighted on this basis, which overcomes the shortcomings of the simple Euclidean distance measurement method. So, it not only has a good tolerance for data noise to solve the misclassification of noise information in the actual data, but also takes into account the difference of distinguishability contribution degree of attributes in classification. Thus, the accuracy of clustering is improved, and it also has significance in practical project application. Then, the proposed distance metric is applied into the traditional MST clustering algorithm. Compared with the traditional MST clustering algorithm and other classical clustering algorithms, the results show that the MST algorithm based on the new distance metric is more effective.