Genetic modeling of olive fruit using clustering technique.

Abstract

Data mining is the process of analyzing a quantity of data and finding the relationships between them. Moreover, summarizing them to obtain graphic models, which are recognizable and beneficial to their users by utilizing a set of automated tools to extract knowledge out of its reservoirs without making prior assumptions about what knowledge, might be. The study is a step toward clarifying the principle of the fuzzy clustering algorithm on both practical and theoretical levels. The theoretical section of the research handled with the notion of data clustering and its different types, in addition an illustration of the FCM method. Whereas the practical part, dealt with selection of neighbor FCM method. Furthermore, its application to olive fruits, which were chosen due to the nutritional economic and commercial properties of these olive fruits, depending on some of the properties available in them because those properties belong to more than one variety at the same time, and the detection of ambiguities states when distinguishing between them. As well as knowing, the similarities between them and their complexity to determine the extent of convergence between the olive types. Python language was used to implement the practical side and the algorithm proved highly efficient in determining the genetic characteristics of the olive fruits in the research sample.