In current study, a particle swarm clustering method is suggested for clustering triangular fuzzy data. This clustering method can find fuzzy cluster centers in the proposed method, where fuzzy cluster centers contain more points from the corresponding cluster, the higher clustering accuracy. Also, triangular fuzzy numbers are utilized to demonstrate uncertain data. To compare triangular fuzzy ...

in this research, the framework is presented for unsupervised change detection using multitemporal sar images based on integration clustering and level set methods. spatial correlation between pixels were considered by using contextual information. also as proposed method was used integration of gustafson-kessel clustering techniques (gkc) and level set methods for change detection. using clust...

background: selecting the correct statistical test and data mining method depends highly on the measurement scale of data, type of variables, and purpose of the analysis. different measurement scales are studied in details and statistical comparison, modeling, and data mining methods are studied based upon using several medical examples. we have presented two ordinal–variables clustering exampl...

Data clustering is an ideal way of working with a huge amount of data and looking for a structure in the dataset. In other words, clustering is the classification of the same data; the similarity among the data in a cluster is maximum and the similarity among the data in the different clusters is minimal. The innovation of this paper is a clustering method based on the Crow Search Algorithm (CS...

the objective of this investigation was to evaluate the magnitude of g × e interaction effects on durum wheat grain yield and to identify superior genotypes adapted to the test environments. twenty improved durum wheat genotypes were tested in five locations over three growing seasons. combined anova indicated that the effect of year (y) was significant and that of the location (l) was not, but...