Abstract:Cluster analysis has been widely used for pattern recognition, machine learning, and in other fields. The K-means dynamic clustering algorithm is simple and efficient, which is why it is one of the most commonly used methods of cluster analysis. The beak of cephalopods, comprising hard tissue, has been widely used to determine species and identify populations owing to its stable structure, corrosion resistance, easily observed growth lines, and abundant characteristic information, causing it to have great application prospects. In this study, the K-means dynamic clustering algorithm was used on 150 pairs of Sthenoteuthis oualaniensis beaks within the mantle length range of 120~200 mm. Samples were collected from the northwest Indian Ocean, the tropical eastern Pacific Ocean and the South China Sea from 2014 to 2019. The results showed that S. oualaniensis from the northwest Indian Ocean had the largest beaks, followed by the tropical eastern Pacific Ocean, and those in the South China Sea. The K-means dynamic clustering algorithm showed that S. oualaniensis from the three areas can be well distinguished. We used z-scores to normalize the data the created a 2D beak morphological parameter matrix to randomize the data before we conducted a K-means dynamic clustering analysis with Manhattan distance and Euclidean distance. The total correct discrimination rate was 86.7% and 88.7%, respectively. This study also identified that the geographic regional differences in beak morphology are unlikely to be due to sampling bias. From the location of the clustering center, we concluded that the Manhattan and Euclidean distance algorithms and outlying points will generate deviations from the clustering center. The K-means dynamic clustering algorithm for beaks of the S. oualaniensis has great reference value. We identified improvements that optimize the K-means algorithm to expand capability for universal use. These improvements and a retrieval system will improve our capabilities to identify S. oualaniensis species.
