Raw data are classified using clustering techniques in a reasonable manner to create disjoint clusters.A lot of clustering algorithms based on specific parameters have been proposed to access a high volume of datasets...Raw data are classified using clustering techniques in a reasonable manner to create disjoint clusters.A lot of clustering algorithms based on specific parameters have been proposed to access a high volume of datasets.This paper focuses on cluster analysis based on neutrosophic set implication,i.e.,a k-means algorithm with a threshold-based clustering technique.This algorithm addresses the shortcomings of the k-means clustering algorithm by overcoming the limitations of the threshold-based clustering algorithm.To evaluate the validity of the proposed method,several validity measures and validity indices are applied to the Iris dataset(from the University of California,Irvine,Machine Learning Repository)along with k-means and threshold-based clustering algorithms.The proposed method results in more segregated datasets with compacted clusters,thus achieving higher validity indices.The method also eliminates the limitations of threshold-based clustering algorithm and validates measures and respective indices along with k-means and threshold-based clustering algorithms.展开更多
A novel mercer kernel based fuzzy clustering self-adaptive algorithm is presented. The mercer kernel method is introduced to the fuzzy c-means clustering. It may map implicitly the input data into the high-dimensional...A novel mercer kernel based fuzzy clustering self-adaptive algorithm is presented. The mercer kernel method is introduced to the fuzzy c-means clustering. It may map implicitly the input data into the high-dimensional feature space through the nonlinear transformation. Among other fuzzy c-means and its variants, the number of clusters is first determined. A self-adaptive algorithm is proposed. The number of clusters, which is not given in advance, can be gotten automatically by a validity measure function. Finally, experiments are given to show better performance with the method of kernel based fuzzy c-means self-adaptive algorithm.展开更多
Studies in vitro show that intravascular ultrasound (IVUS) underestimates vessel and lumen dimensions. In order to validate IVUS measurement in vivo, we conducted a comparative study during catheterization in fifty pa...Studies in vitro show that intravascular ultrasound (IVUS) underestimates vessel and lumen dimensions. In order to validate IVUS measurement in vivo, we conducted a comparative study during catheterization in fifty patients. The patients underwent IVUS examinations for the purpose of diagnosis or treatment of coronary artery disease. The IVUS system was a 3.5 F, 20 MHz IVUS catheter (Sonicath catheter of Boston Scientific Co.) and a Hewlett Packard console. After examination of the coronary artery, the IVUS probe was withdrawn back into the guiding catheter to measure the average lumen diameter of the guiding catheter (8 F, Cordis). This measurement in vivo (VI) was compared with the true lumen diameter provided by the manufac Department of Cardiology, University of Essen, Essen, Germany (Liu FQ, Ge JB, Baumgart D, Haude M, Caspari G, Grge G, Eick B and Erbel R) turer (MA) and determined by on line quantitative angiography (HICOR, Siemens) (HI). In addition, the IVUS measurement in vitro (VT) was also taken with the same guiding catheter in waterbath at 37℃. The results showed that IVUS underestimated the true lumen diameter by 2.2%±2.6% in vivo, by 3.1%±1.8% in vitro, while HICOR owerestimated the true lumen diameter by 23.0%±6.8%. There was no difference between the IVUS measurements in vivo and in vitro. In summary, IVUS was very accurate for the measurement of a 8 F guiding catheter in vivo with only a minor underestimation, and IVUS measurement was far more reliable than the on line quantitative angiography.展开更多
The popular single-factor complexity measure cannot comprehensively reflect program complexity and the existing hybrid complexity measure cannot express the interactive behaviors of programs. To treat these problems, ...The popular single-factor complexity measure cannot comprehensively reflect program complexity and the existing hybrid complexity measure cannot express the interactive behaviors of programs. To treat these problems, in this paper, we propose a complexity measure based on program slicing（CMBPS）. CMPBS not only can evaluate factors which affect program complexity such as the length of the program, control flow, data flow and data types of output variables, but also can give expression of the interactive relation between programs. And we also prove that CMBPS satisfies all of Weyuker properties. Compared with the popular complexity measures, CMBPS is a well-structured complexity measure.展开更多
文摘Raw data are classified using clustering techniques in a reasonable manner to create disjoint clusters.A lot of clustering algorithms based on specific parameters have been proposed to access a high volume of datasets.This paper focuses on cluster analysis based on neutrosophic set implication,i.e.,a k-means algorithm with a threshold-based clustering technique.This algorithm addresses the shortcomings of the k-means clustering algorithm by overcoming the limitations of the threshold-based clustering algorithm.To evaluate the validity of the proposed method,several validity measures and validity indices are applied to the Iris dataset(from the University of California,Irvine,Machine Learning Repository)along with k-means and threshold-based clustering algorithms.The proposed method results in more segregated datasets with compacted clusters,thus achieving higher validity indices.The method also eliminates the limitations of threshold-based clustering algorithm and validates measures and respective indices along with k-means and threshold-based clustering algorithms.
文摘A novel mercer kernel based fuzzy clustering self-adaptive algorithm is presented. The mercer kernel method is introduced to the fuzzy c-means clustering. It may map implicitly the input data into the high-dimensional feature space through the nonlinear transformation. Among other fuzzy c-means and its variants, the number of clusters is first determined. A self-adaptive algorithm is proposed. The number of clusters, which is not given in advance, can be gotten automatically by a validity measure function. Finally, experiments are given to show better performance with the method of kernel based fuzzy c-means self-adaptive algorithm.
文摘Studies in vitro show that intravascular ultrasound (IVUS) underestimates vessel and lumen dimensions. In order to validate IVUS measurement in vivo, we conducted a comparative study during catheterization in fifty patients. The patients underwent IVUS examinations for the purpose of diagnosis or treatment of coronary artery disease. The IVUS system was a 3.5 F, 20 MHz IVUS catheter (Sonicath catheter of Boston Scientific Co.) and a Hewlett Packard console. After examination of the coronary artery, the IVUS probe was withdrawn back into the guiding catheter to measure the average lumen diameter of the guiding catheter (8 F, Cordis). This measurement in vivo (VI) was compared with the true lumen diameter provided by the manufac Department of Cardiology, University of Essen, Essen, Germany (Liu FQ, Ge JB, Baumgart D, Haude M, Caspari G, Grge G, Eick B and Erbel R) turer (MA) and determined by on line quantitative angiography (HICOR, Siemens) (HI). In addition, the IVUS measurement in vitro (VT) was also taken with the same guiding catheter in waterbath at 37℃. The results showed that IVUS underestimated the true lumen diameter by 2.2%±2.6% in vivo, by 3.1%±1.8% in vitro, while HICOR owerestimated the true lumen diameter by 23.0%±6.8%. There was no difference between the IVUS measurements in vivo and in vitro. In summary, IVUS was very accurate for the measurement of a 8 F guiding catheter in vivo with only a minor underestimation, and IVUS measurement was far more reliable than the on line quantitative angiography.
基金Supported by the National High Technology Research and Development Program of China(863 Program)(2009AA01220)the National Natural Science Foundation of China(91118007)
文摘The popular single-factor complexity measure cannot comprehensively reflect program complexity and the existing hybrid complexity measure cannot express the interactive behaviors of programs. To treat these problems, in this paper, we propose a complexity measure based on program slicing（CMBPS）. CMPBS not only can evaluate factors which affect program complexity such as the length of the program, control flow, data flow and data types of output variables, but also can give expression of the interactive relation between programs. And we also prove that CMBPS satisfies all of Weyuker properties. Compared with the popular complexity measures, CMBPS is a well-structured complexity measure.
