An Automatic Clustering Algorithm Using NSGA-II with Gene Rearrangement

2020 ◽  
Author(s):  
Hongchun Qu ◽  
Li Yin
Keyword(s):  
Gene Rearrangement ◽  
Clustering Algorithm ◽  
Nsga Ii ◽  
Automatic Clustering

Segmentation of SAR Image using Fuzzy C-Means and Filters

2020 ◽  
Vol 8 (1) ◽  
pp. 84-90
Author(s):  
R. Lalchhanhima ◽  
◽  
Debdatta Kandar ◽  
R. Chawngsangpuii ◽  
Vanlalmuansangi Khenglawt ◽  
...  
Keyword(s):  
Clustering Algorithm ◽  
State Of The Art ◽  
Speckle Noise ◽  
Synthetic Aperture Radar Image ◽  
Synthetic Aperture ◽  
Sar Image ◽  
Spatial Filters ◽  
Fuzzy C Means ◽  
Automatic Clustering ◽  
Intensity Information

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.

A population-based automatic clustering algorithm for image segmentation

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

An efficient robust automatic clustering algorithm for interval data

2021 ◽  
pp. 1-15
Author(s):  
Tai Vo-Van ◽  
Lethikim Ngoc ◽  
Thao Nguyen-Trang
Keyword(s):  
Clustering Algorithm ◽  
Interval Data ◽  
Automatic Clustering

Multi-objective Automatic Clustering with Gene Rearrangement and Cluster Merging

2021 ◽  
pp. 87-127
Author(s):  
Hongchun Qu ◽  
Li Yin ◽  
Xiaoming Tang
Keyword(s):  
Gene Rearrangement ◽  
Multi Objective ◽  
Automatic Clustering ◽  
Cluster Merging

Variable penalty factors: a new GEP automatic clustering algorithm

2017 ◽  
Vol 15 (1/2) ◽  
pp. 3
Author(s):  
Yan Chen ◽  
Kangshun Li ◽  
Haohua Huang
Keyword(s):  
Clustering Algorithm ◽  
Automatic Clustering

scholarly journals An Adaptive Multiobjective Genetic Algorithm with Fuzzy c-Means for Automatic Data Clustering

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Ze Dong ◽  
Hao Jia ◽  
Miao Liu
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Majority Vote ◽  
Clustering Algorithms ◽  
Nsga Ii ◽  
Number Of Clusters ◽  
Automatic Data ◽  
Multiobjective Genetic Algorithm ◽  
Fuzzy Clustering Method

This paper presents a fuzzy clustering method based on multiobjective genetic algorithm. The ADNSGA2-FCM algorithm was developed to solve the clustering problem by combining the fuzzy clustering algorithm (FCM) with the multiobjective genetic algorithm (NSGA-II) and introducing an adaptive mechanism. The algorithm does not need to give the number of clusters in advance. After the number of initial clusters and the center coordinates are given randomly, the optimal solution set is found by the multiobjective evolutionary algorithm. After determining the optimal number of clusters by majority vote method, the Jm value is continuously optimized through the combination of Canonical Genetic Algorithm and FCM, and finally the best clustering result is obtained. By using standard UCI dataset verification and comparing with existing single-objective and multiobjective clustering algorithms, the effectiveness of this method is proved.

scholarly journals Wideband Channel Modeling for mm-Wave inside Trains for 5G-Related Applications

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Juan Moreno García-Loygorri ◽  
César Briso-Rodríguez ◽  
Israel Arnedo ◽  
César Calvo ◽  
Miguel A. G. Laso ◽  
...  
Keyword(s):  
Clustering Algorithm ◽  
Path Loss ◽  
Channel Modeling ◽  
Wireless Channel ◽  
Full Description ◽  
Model Parameters ◽  
Time Of Arrival ◽  
Traffic Demand ◽  
Automatic Clustering ◽  
Wideband Channel

Passenger trains and especially metro trains have been identified as one of the key scenarios for 5G deployments. The wireless channel inside a train car is reported in the frequency range between 26.5 GHz and 40 GHz. These bands have received a lot of interest for high-density scenarios with a high-traffic demand, two of the most relevant aspects of a 5G network. In this paper we provide a full description of the wideband channel estimating Power-Delay Profiles (PDP), Saleh-Valenzuela model parameters, time-of-arrival (TOA) ranging, and path-loss results. Moreover, the performance of an automatic clustering algorithm is evaluated. The results show a remarkable degree of coherence and general conclusions are obtained.

Variable penalty factors: a new GEP automatic clustering algorithm

2017 ◽  
Vol 15 (1/2) ◽  
pp. 3
Author(s):  
Haohua Huang ◽  
Yan Chen ◽  
Kangshun Li
Keyword(s):  
Clustering Algorithm ◽  
Automatic Clustering
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