Normalization-Based Validity Index of Adaptive K-Means Clustering for Multi-Solution Application

Validity evaluation aims to analyze the quality of the clustering algorithm with different measurement criteria. A variety of assessment methods have been introduced in the application of pattern recognition and computer vision. Although it is well known that mining information of massive data is essential, most of the validity indices only provide a single partitioning scheme for clustering validation. Moreover, the conventional evaluation algorithm is susceptible to the density and dimension of the dataset, which leads to assessment failure. In this paper, a normalization-based validity index (NbVI) is proposed for validity evaluation of the adaptive K-means clustering from a multi-solution perspective. According to the concept of high-compact within clusters and high-separation among groups, NbVI attempts to find the maximum relative ratio between normalized inter-distance and normalized intra-distance. The experimental results demonstrate that the proposed NbVI method exhibits excellent performance for the clustering of the density-unbalanced dataset for multi-solution applications. Moreover, the NbVI validation shows high versatility using different clustering algorithms.