Integrating Type-1 Fuzzy and Type-2 Fuzzy Clustering with K-Means for Pre-Processing Input Data in Classification Algorithms
نویسندگان
چکیده
منابع مشابه
Integrating type-1 fuzzy and type-2 fuzzy clustering with k-means for pre-processing input data in classification algorithms
In several papers, clustering has been used for preprocessing datasets before applying classification algorithms in order to enhance classification results. A strong clustered dataset as input to classification algorithms can significantly improve the computation time. This can be particularly useful in “Big Data” where computation time is equally or more important than accuracy. However, there...
متن کاملA Fuzzy C-means Algorithm for Clustering Fuzzy Data and Its Application in Clustering Incomplete Data
The fuzzy c-means clustering algorithm is a useful tool for clustering; but it is convenient only for crisp complete data. In this article, an enhancement of the algorithm is proposed which is suitable for clustering trapezoidal fuzzy data. A linear ranking function is used to define a distance for trapezoidal fuzzy data. Then, as an application, a method based on the proposed algorithm is pres...
متن کاملa cauchy-schwarz type inequality for fuzzy integrals
نامساوی کوشی-شوارتز در حالت کلاسیک در فضای اندازه فازی برقرار نمی باشد اما با اعمال شرط هایی در مسئله مانند یکنوا بودن توابع و قرار گرفتن در بازه صفر ویک می توان دو نوع نامساوی کوشی-شوارتز را در فضای اندازه فازی اثبات نمود.
15 صفحه اولIntegrating Fuzzy C-Means Clustering Technique with K-Means Clustering Technique for CBIR
Image database sizes have increased enormously in the recent years due to the development of the technology which has developed the need for Content Based Image Retrieval (CBIR) system. In this study a CBIR system that allows searching and retrieves images from the databases is developed using the fuzzy c-means algorithm and K-means clustering, the system uses the low level features like color,...
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: International Journal of Intelligent Information Systems
سال: 2014
ISSN: 2328-7675
DOI: 10.11648/j.ijiis.s.2014030601.27