Application of genetic and K-means algorithms in clustering Babakoohi Anticline joints north of Shiraz, Iran

Abstract

The fuzzy clustering technique is one of the ways of organizing data that presents special patterns using algorithms and based on the similarity level of data. In this study, in order to cluster the resulting data from the Babakoohi Anticline joints, located north of Shiraz, K-means and genetic algorithms are applied. The K-means algorithm is one of the clustering algorithms easily implemented and of fast performance; however, sometimes this algorithm is located in the local optimal trap and cannot respond with an optimal answer, due to the sensitivity of this algorithm to the centers of the primary cluster. In addition, it has some basic disadvantages, such as its inappropriateness for complicated forms and also the dependency of the final result upon the primary cluster. Therefore, in order to perform the study more accurately and to obtain more reliable results, the genetic algorithm is used for categorizing the data of joints of the studied area. Applying this algorithm for leaving the local optimal points is an effective way. The results of clustering of the aforementioned data using the two above techniques represent two clusters in the Babakoohi Anticline. Furthermore, for validity and surveying of the results of the suggested techniques, various mathematical and statistical techniques, including ICC, Vw, VMPC, and VPMBF, are applied, which supports the similarity of the obtained results and the data clustering process in two algorithms.