Statistical Clustering Algorithm of Wild Animal Growth Environment

In the new era, people's awareness of protecting the ecological environment is increasing. But nowadays, the growth environment of wild animals is more complicated, and it is difficult to carry out statistical work. Therefore, it is quite necessary to carry out research on the statistical clust...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Revista científica (Universidad del Zulia. Facultad de Ciencias Veterinarias. División de Investigación) Ročník 30; číslo 1; s. 457
Hlavný autor: Pan, Lijing
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Universidad del Zulia, Facultad de Ciencias Veterinarias 01.01.2020
Predmet:
China
ISSN:0798-2259
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:In the new era, people's awareness of protecting the ecological environment is increasing. But nowadays, the growth environment of wild animals is more complicated, and it is difficult to carry out statistical work. Therefore, it is quite necessary to carry out research on the statistical clustering algorithm of the environment for the growth of wild animals. The purpose of this article is to explore the statistical clustering algorithm for the growth environment of wild animals. By selecting the wild animal monitoring data in May in areas A, B, and C for processing, the FCM algorithm and k-means algorithm are used to analyze the data. Cluster analysis of 90 sets of wild animal environment monitoring sample data, and then compare the execution efficiency and accuracy of the two algorithms. The research results show that the k-means algorithm aggregates 90 samples of wild animal environmental monitoring data into the correct area, 76 of them, and the FCM algorithm clusters correctly of 80. The clustering results of the FCM algorithm are more accurate than k-means, because the clustering results of the FCM algorithm have only a small part of the data of the A region in the third category (C region). In the k-means clustering results, the data of the second type (C region) includes some data of regions A and B. Key words: Wild Animals, Growth Environment, Data Statistics, Clustering Algorithms
ISSN:0798-2259