Continuous attribute reduction method based on an automatic clustering algorithm and decision entropy

Fuzzy C-Means is an unsupervised clustering algorithm for the automatic clustering of data. Synthetic Aperture Radar Image Segmentation has been a challenging task because of the presence of speckle noise. Therefore the segmentation process can not directly rely on the intensity information alone but must consider several derived features in order to get satisfactory segmentation results. In this paper, it is attempted to use the fuzzy nature of classification for the purpose of unsupervised region segmentation in which FCM is employed. Different features are obtained by filtering of the image by using different spatial filters and are selected for segmentation criteria. The segmentation performance is determined by the accuracy compared with a different state of the art techniques proposed recently.

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A population-based automatic clustering algorithm for image segmentation

Proceedings of the Genetic and Evolutionary Computation Conference Companion ◽

10.1145/3449726.3463148 ◽

2021 ◽

Author(s):

Seyed Jalaleddin Mousavirad ◽

Gerald Schaefer ◽

Mahshid Helali Moghadam ◽

Mehrdad Saadatmand ◽

Mahdi Pedram

Keyword(s):

Image Segmentation ◽

Clustering Algorithm ◽

Population Based ◽

Automatic Clustering

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An automatic clustering algorithm based on the density-peak framework and Chameleon method

Pattern Recognition Letters ◽

10.1016/j.patrec.2021.06.017 ◽

2021 ◽

Author(s):

Zhou Liang ◽

Pei Chen

Keyword(s):

Clustering Algorithm ◽

Density Peak ◽

Automatic Clustering

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Attribute Reduction Method and Its Application using Bat Algorithm and Rough Sets

10.1145/3467707.3467745 ◽

2021 ◽

Author(s):

Shangzhi Wu ◽

Huanhuan Wang ◽

Ning Xia ◽

Fan Zhang

Keyword(s):

Rough Sets ◽

Reduction Method ◽

Attribute Reduction ◽

Bat Algorithm

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An attribute reduction method based on core samples set

2012 International Conference on Machine Learning and Cybernetics ◽

10.1109/icmlc.2012.6358912 ◽

2012 ◽

Author(s):

Eric C.C. Tsang ◽

Chen-Xia Jin ◽

De-Gang Chen ◽

Fa-Chao Li

Keyword(s):

Reduction Method ◽

Attribute Reduction ◽

Core Samples

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Attribute Reduction Based on Consistent Covering Rough Set and Its Application

Complexity ◽

10.1155/2017/8986917 ◽

2017 ◽

Vol 2017 ◽

pp. 1-9 ◽

Cited By ~ 6

Author(s):

Jianchuan Bai ◽

Kewen Xia ◽

Yongliang Lin ◽

Panpan Wu

Keyword(s):

Set Theory ◽

Rough Set ◽

Rough Set Theory ◽

Attribute Reduction ◽

Relevance Vector Machine ◽

Support Vector ◽

Processing Step ◽

Important Processing ◽

Continuous Attribute ◽

Covering Rough Set

As an important processing step for rough set theory, attribute reduction aims at eliminating data redundancy and drawing useful information. Covering rough set, as a generalization of classical rough set theory, has attracted wide attention on both theory and application. By using the covering rough set, the process of continuous attribute discretization can be avoided. Firstly, this paper focuses on consistent covering rough set and reviews some basic concepts in consistent covering rough set theory. Then, we establish the model of attribute reduction and elaborate the steps of attribute reduction based on consistent covering rough set. Finally, we apply the studied method to actual lagging data. It can be proved that our method is feasible and the reduction results are recognized by Least Squares Support Vector Machine (LS-SVM) and Relevance Vector Machine (RVM). Furthermore, the recognition results are consistent with the actual test results of a gas well, which verifies the effectiveness and efficiency of the presented method.

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