Genetic Modeling of Olive Fruit Using Clustering Technique

Data mining is the process of analyzing a quantity of data and finding relationships between them. It is summarized to obtain recognizable and useful graphic models for its users through the use of a set of automated tools to extract knowledge from its potential without making prior assumptions. The study is a step towards clarifying the principle of the fuzzy assembly algorithm on the practical and theoretical levels. The theoretical section of the research dealt with the concept of data collection and its different types, in addition to an explanation of the FCM method. While the practical part, deal with the selection of the adjacent FCM method. And the olive fruits were chosen in the practical aspect because of the nutritional, economic and commercial characteristics of this fruit, depending on some of the characteristics available in it, as those characteristics belong to more than one variety at the same time. The work of the algorithm is to detect ambiguities between the varieties under study and then distinguish between them. As well as knowing the similarities between them to determine the extent of similarity between the types of olives. Its purpose is to expand the geographical area of olive cultivation and to find new hybrid varieties that have high-quality features. The researchers used the Python language to implement the practical side, and the algorithm proved highly efficient in determining the genetic characteristics of olive fruits in the research sample, The most important finding of the research is that the FCM algorithm was very flexible in dealing with different types of data and incomplete data processing, as well as its smoothness in dealing with different systems and programs.