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

• Published in: IEEE access Vol. 8; pp. 9403 - 9419
• Main Authors: Li, Tao, Ma, Yitao, Endoh, Tetsuo
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Cluster analysis; Clustering; Clustering algorithms; Computer vision; Data mining; Datasets; Density; Indexes; Internet of Things; K-means clustering; multi-solution; NbVI; Partitioning algorithms; Pattern recognition; Principal component analysis; Social networking (online); unsupervised learning; Validity; validity index; Vector quantization
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.2964763

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.