An Overview of Semi-Supervised Fuzzy Clustering Algorithms
نویسندگان
چکیده
منابع مشابه
An Overview of Unsupervised and Semi-Supervised Fuzzy Kernel Clustering
For real-world clustering tasks, the input data is typically not easily separable due to the highly complex data structure or when clusters vary in size, density and shape. Kernel-based clustering has proven to be an effective approach to partition such data. In this paper, we provide an overview of several fuzzy kernel clustering algorithms. We focus on methods that optimize an fuzzy C-mean-ty...
متن کاملAn Improved Semi-supervised Fuzzy Clustering Algorithm
Semi-supervised clustering is an important method which can improve clustering performance by introducing partial supervised information. This paper mainly studies the semi-supervised fuzzy clustering based on Mahalanobis distance and Gaussian Kernel for SCAPC algorithm. Here, we give a new semi-supervised fuzzy clustering objective function. By solving the optimization problem with above objec...
متن کاملActive 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 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 کاملSemi-supervised Clustering in Fuzzy Rule Generation
Inductive learning approaches traditionally categorized as supervised, which use labeled data sets, and unsupervised, which use unlabeled data sets in learning tasks. The great volume of available data and the cost involved in manual labeling has motivated the investigation of different solutions for machine learning tasks related to unlabeled data. The approach proposed here fits into this con...
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: International Journal of Engineering and Technology
سال: 2016
ISSN: 1793-8236
DOI: 10.7763/ijet.2016.v8.902