@article { author = {Bijanzadeh, E. and Emam, Y. and Ebrahimi, E. and Ebrahimi, M.}, title = {Application of unsupervised weighting algorithms for identifying important attributes and factors contributing to grain and biological yields of wheat}, journal = {Crop Breeding Journal}, volume = {2}, number = {2}, pages = {111-117}, year = {2012}, publisher = {Seed and Plant Improvement Institute}, issn = {2008-868X}, eissn = {2423-4605}, doi = {10.22092/cbj.2012.100427}, abstract = {To identify important attributes/factors that contribute to grain and biological yields of wheat, 9912 sets of diverse data from field studies were extracted, and supervised attribute-weighting models were employed. Results showed that when biological yield was the output, grain yield, nitrogen applied, rainfall, irrigation regime, and organic content were the most important factors/attributes, highlighted by 9, 7, 5, 3 and 3 weighting models, respectively. In contrast, when grain yield was the output, biological yield, location, and genotype were identified by 8, 6, and 5 weighting models, respectively. Also, five other features (cropping system, organic content, 1000-grain weight, spike number m-2 and soil texture) were selected by three models as the most important factors/attributes. Field water status, such as the irrigation regime or the amount of rainfall, was another important factor related to the biological or grain yield of wheat (weight ≥ 0.5). Our results showed that attribute/factor classification by unsupervised attribute-weighting models can provide a comprehensive view of the important distinguishing attributes/factors that contribute to wheat grain or biological yield. This is the first report on identifying the most important factors/attributes contributing to wheat grain and biological yields-using attribute-weighting algorithms. This study opened a new horizon in wheat production using data mining.}, keywords = {attribute weighting,Data Mining,unsupervised model,Wheat}, url = {https://cbjournal.areeo.ac.ir/article_100427.html}, eprint = {https://cbjournal.areeo.ac.ir/article_100427_8c7511bf4fc18c57533a93112f7e585d.pdf} }