فهرست مقالات Kmeans clustering

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  1 - Experimental Evaluation of Algorithmic Effort Estimation Models using Projects Clustering
  Farzaneh Famoori Vahid Khatibi bardsiri Shima Javadi Moghadam Fakhrosadat Fanian
  One of the most important aspects of software project management is the estimation of cost and time required for running information system. Therefore, software managers try to carry estimation based on behavior, properties, and project restrictions. Software cost estim چکیده کامل

  One of the most important aspects of software project management is the estimation of cost and time required for running information system. Therefore, software managers try to carry estimation based on behavior, properties, and project restrictions. Software cost estimation refers to the process of development requirement prediction of software system. Various kinds of effort estimation patterns have been presented in recent years, which are focused on intelligent techniques. This study made use of clustering approach for estimating required effort in software projects. The effort estimation is carried out through SWR (StepWise Regression) and MLR (Multiple Linear Regressions) regression models as well as CART (Classification And Regression Tree) method. The performance of these methods is experimentally evaluated using real software projects. Moreover, clustering of projects is applied to the estimation process. As indicated by the results of this study, the combination of clustering method and algorithmic estimation techniques can improve the accuracy of estimates. پرونده مقاله
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  2 - Diagnosis of Liver Cancer by Fuzzy Kmeans Clustering Based on Evidence Theory
  Babak Fouladi Nia Abbas Karimi Faraneh Zarafshan Manochehr Kazemi
  10.30495/ijsee.2022.1948054.1163
  Liver cancer is one of the most common cancers that causes many deaths every year. In recent years, the risk of men and women getting liver cancer has increased by 40% and 23%, respectively. In order to identify a tumor in the liver, segmentation is performed on CT imag چکیده کامل

  Liver cancer is one of the most common cancers that causes many deaths every year. In recent years, the risk of men and women getting liver cancer has increased by 40% and 23%, respectively. In order to identify a tumor in the liver, segmentation is performed on CT images. The use of data fusion methods in data mining techniques is one of the most practical methods to improve accuracy, which also has many applications in the field of medical image processing. Correct and efficient diagnosis of liver abnormalities leads to a significant reduction in human error and a more accurate diagnosis by physicians. This requires the use of methods based on automatic and semi-automatic detection. Combining clustering methods and considering cluster uncertainty is an appropriate tool in solving clustering problems in medical image processing, especially cancer diagnosis. The proposed method, in addition to having high accuracy, has a high convergence speed. پرونده مقاله