A Relaxed Fuzzy ISODATA Algorithm
نویسنده
چکیده
In this research, the traditional fuzzy ISODATA ( FI ) algorithm is integrated with probabilistic relaxation labeling ( PRL ) algorithm to form a new clustering algorithm called relaxed fuzzy ISODATA ( RFI ). During the clustering process, both the fuzzy membership function values and local contextual information are employed for grouping data into clusters. The RFI algorithm is considered for clustering noisy data. The experimental results shows the effectiveness of this algorithm.
منابع مشابه
Comparison of FCM and FISODATA
In fuzzy clustering, the fuzzy c-means (FCM) clustering algorithm is the best known and used method. An interesting extension of FCM is the fuzzy ISODATA (FISODATA) algorithm; it updates cluster number during the algorithm. That's why we can have more or less clusters than the initialization step. It's the power of the fuzzy ISODATA algorithm comparing to FCM. The aim of this paper is...
متن کاملAn Adaptive Clustering Algorithm Based on the Possibility Clustering and Isodata for Multispectral Image Classification
For a clustering algorithm, the number of clusters is a key parameter since it is directly related to the number of homogenous regions in the given image. Although ISODATA clustering algorithm can determine the number of clusters and cluster centers dynamically, it is challenging to specify so many parameters. The possibility clustering provides the memberships that are interpreted as the degre...
متن کاملAn eYcient neural classi cation chain of SAR and optical urban images
In this paper a suitable neural classi cation algorithm, based on the use of Adaptive Resonance Theory (ART) networks, is applied to the fusion and classi cation of optical and SAR urban images. ART networks provide a exible tool for classi cation, but are ruled by a large number of parameters. Therefore, the simpli ed ART2-A algorithm is used in this paper, and the neural approach is int...
متن کاملFuzzy clustering with partial supervision
Presented here is a problem of fuzzy clustering with partial supervision, i.e., unsupervised learning completed in the presence of some labeled patterns. The classification information is incorporated additively as a part of an objective function utilized in the standard FUZZY ISODATA. The algorithms proposed in the paper embrace two specific learning scenarios of complete and incomplete class ...
متن کاملFuzzy EMG classification for prosthesis control.
This paper proposes a fuzzy approach to classify single-site electromyograph (EMG) signals for multifunctional prosthesis control. While the classification problem is the focus of this paper, the ultimate goal is to improve myoelectric system control performance, and classification is an essential step in the control. Time segmented features are fed to a fuzzy system for training and classifica...
متن کامل