Automatic clustering algorithm for interval data based on overlap distance

Abstract

In this study, the improved overlap distance is used as a criterion in order to build clusters for interval data. This distance has shown the suitability, and given an outstanding advantage in evaluating the similarity for intervals with a lot of the considered data sets. Based on the overlap distance, we propose the Automatic Clustering Algorithm for Interval data (ACAI). One of the best advantages of the proposed algorithm is that ACAI figure out simultaneously the appropriate number of groups, and factors in every group. The proposed algorithm can be effectively performed through a Matlab procedure. Based on the extracted intervals from texture of images, we have applied ACAI to recognize the images, an interesting and challenging issue at present. Experimental data sets including the differences of the characteristics as well as the number of elements has shown the reasonableness of the proposed algorithm, and its advantages in comparing to the surviving ones. From the image recognition problem, this research has shown prospect in practical applications for many fields.

Keywords:

• Automatic
• Cluster analysis
• CR index
• Interval data
• Overlap distance

Additional information

Funding

For Tuan Lehoang ([email protected]), this research is funded by Vietnam National University Ho Chi Minh City (VNU-HCM) under grant number C2021-26-02.