Robust Rough-Fuzzy C-Means Algorithm: Design and Applications in Coding and Non-coding RNA Expression Data Clustering

2013 ◽  
Vol 124 (1-2) ◽  
pp. 153-174 ◽  
Author(s):  
Pradipta Maji ◽  
Sushmita Paul
Keyword(s):  
Data Clustering ◽  
Algorithm Design ◽  
Rna Expression ◽  
Expression Data ◽  
Fuzzy C Means ◽  
Non Coding Rna ◽  
Fuzzy C Means Algorithm

CLUSTERING GENE EXPRESSION DATA WITH KERNEL PRINCIPAL COMPONENTS

2005 ◽  
Vol 03 (02) ◽  
pp. 303-316 ◽  
Author(s):  
ZHENQIU LIU ◽  
DECHANG CHEN ◽  
HALIMA BENSMAIL ◽  
YING XU
Keyword(s):  
Gene Expression ◽  
Principal Component Analysis ◽  
Gene Expression Data ◽  
Microarray Data ◽  
Principal Components ◽  
Data Clustering ◽  
Principal Component ◽  
Kernel Principal Component Analysis ◽  
Expression Data ◽  
Fuzzy C Means

Kernel principal component analysis (KPCA) has been applied to data clustering and graphic cut in the last couple of years. This paper discusses the application of KPCA to microarray data clustering. A new algorithm based on KPCA and fuzzy C-means is proposed. Experiments with microarray data show that the proposed algorithms is in general superior to traditional algorithms.

Classification of Remote Sensing Imagery Based on Density and Fuzzy c-Means Algorithm

2019 ◽  
Vol 8 (2) ◽  
pp. 1-15 ◽  
Author(s):  
Trinh Le Hung ◽  
Mai Dinh Sinh
Keyword(s):  
Classification Accuracy ◽  
Data Clustering ◽  
Minimum Distance ◽  
Satellite Image ◽  
Clustering Methods ◽  
Cluster Centroid ◽  
Fuzzy C Means ◽  
Advantages And Disadvantages ◽  
Fuzzy C Means Algorithm

The goal of data clustering is to divide a set of data into different clusters, so that the data in the same cluster show some similar characteristics. There are many clustering methods for satellite image segmentation, such as k-means, c-means, iso-data, minimum distance algorithms. Each method has certain advantages and disadvantages, but generally they are based on brightness value to divide the pixels of the image in to clusters. Actually, the probability of occurrence of frequency of appearance of pixel has certain effects on clustering results. In this article, the authors propose a method for clustering satellite imagery based on density. It consists of two main steps: find cluster centroid using density and data clustering using fuzzy c-Means algorithm (DFCM). The results obtained in this study can be used to potentially improve classification accuracy of satellite image.

Long non-coding RNA expression profiles in human parathyroid tumors

2017 ◽  
Author(s):  
Annamaria Morotti ◽  
Irene Forno ◽  
Valentina Andre ◽  
Andrea Terrasi ◽  
Chiara Verdelli ◽  
...  
Keyword(s):  
Expression Profiles ◽  
Rna Expression ◽  
Parathyroid Tumors ◽  
Non Coding Rna ◽  
Long Non Coding Rna
Sign in / Sign up

Export Citation Format

Share Document