UNSUPERVISED LEARNING FOR THE IDENTIFICATION OF HOMOGENEOUS FOREST LANDSCAPES

Abstract

This study focuses on collecting information about Djebel messaad Forest from various sources and integrating it into a database. The importance of effectively analyzing this data to achieve specific objectives is noted. The database is considered a crucial source for leveraging available data, requiring the use of appropriate analytical techniques to extract valuable insights. The memorandum explores the use of K-Means clustering and hierarchical algorithms as primary tools for data analysis. The goal of applying clustering algorithms is to group data into clusters characterized by maximum similarity within plant and flower data in each cluster, and maximum dissimilarity between different clusters. Through the analysis of data using these algorithms, we were able to achieve satisfactory results that contribute to a better understanding of the data and the attainment of specific objectives.