Semi-Supervised Deep Fuzzy C-Mean Clustering for Software Fault Prediction
نویسندگان
چکیده
منابع مشابه
Active semi-supervised fuzzy clustering
Clustering algorithms are increasingly employed for the categorization of image databases, in order to provide users with database overviews and make their access more effective. By including information provided by the user, the categorization process can produce results that come closer to user’s expectations. To make such a semi-supervised categorization approach acceptable for the user, thi...
متن کاملAnalysis of Software Fault and Defect Prediction by Fuzzy C-Means Clustering and Adaptive Neuro Fuzzy C-Means Clustering
Faults are related to failures and they do not have much power for indicating a higher quality or a better system above the baseline that the end-users expect.The system faults are the defects that brim in executable files. Conventional approaches employ the experts to navigate directly into the source code errors. However expansion in system size grew the complexity of task exponentially and g...
متن کاملDeep Transductive Semi-supervised Maximum Margin Clustering
Semi-supervised clustering is an very important topic in machine learning and computer vision. The key challenge of this problem is how to learn a metric, such that the instances sharing the same label are more likely close to each other on the embedded space. However, little attention has been paid to learn better representations when the data lie on non-linear manifold. Fortunately, deep lear...
متن کاملSemi-Supervised Fuzzy Clustering with Feature Discrimination
Semi-supervised clustering algorithms are increasingly employed for discovering hidden structure in data with partially labelled patterns. In order to make the clustering approach useful and acceptable to users, the information provided must be simple, natural and limited in number. To improve recognition capability, we apply an effective feature enhancement procedure to the entire data-set to ...
متن کاملImage Segmentation: Type–2 Fuzzy Possibilistic C-Mean Clustering Approach
Image segmentation is an essential issue in image description and classification. Currently, in many real applications, segmentation is still mainly manual or strongly supervised by a human expert, which makes it irreproducible and deteriorating. Moreover, there are many uncertainties and vagueness in images, which crisp clustering and even Type-1 fuzzy clustering could not handle. Hence, Type-...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Access
سال: 2018
ISSN: 2169-3536
DOI: 10.1109/access.2018.2835304