A coevolving memetic algorithm for simultaneous partitional clustering and feature weighting

2013 ◽  
Author(s):  
Yiwen Sun ◽  
Zexuan Zhu ◽  
Shan He ◽  
Zhen Ji
Keyword(s):  
Memetic Algorithm ◽  
Feature Weighting ◽  
Partitional Clustering

scholarly journals A survey on feature weighting based K-Means algorithms

2020 ◽  
Author(s):  
Renato Cordeiro de Amorim
Keyword(s):  
Machine Learning ◽  
Empirical Evidence ◽  
Real World ◽  
Clustering Algorithm ◽  
Machine Learning Algorithms ◽  
Feature Weighting ◽  
Future Research ◽  
Real World Data ◽  
Data Set ◽  
Partitional Clustering

In a real-world data set there is always the possibility, rather high in our opinion, that different features may have different degrees of relevance. Most machine learning algorithms deal with this fact by either selecting or deselecting features in the data preprocessing phase. However, we maintain that even among relevant features there may be different degrees of relevance, and this should be taken into account during the clustering process. With over 50 years of history, K-Means is arguably the most popular partitional clustering algorithm there is. The first K-Means based clustering algorithm to compute feature weights was designed just over 30 years ago. Various such algorithms have been designed since but there has not been, to our knowledge, a survey integrating empirical evidence of cluster recovery ability, common flaws, and possible directions for future research. This paper elaborates on the concept of feature weighting and addresses these issues by critically analysing some of the most popular, or innovative, feature weighting mechanisms based in K-Means

Deployment Optimization for Point Air Defense Based on Memetic Algorithm

2010 ◽  
Vol 36 (2) ◽  
pp. 242-248 ◽  
Author(s):  
Jie CHEN ◽  
Chen CHEN ◽  
Juan ZHANG ◽  
Bin XIN
Keyword(s):  
Memetic Algorithm ◽  
Air Defense ◽  
Deployment Optimization

An energy aware grouping memetic algorithm to schedule the sensing activity in WSNs-based IoT for smart cities

2021 ◽  
pp. 107473
Author(s):  
Mohammad Bagher Dowlatshahi ◽  
Marjan Kuchaki Rafsanjani ◽  
Brij B. Gupta
Keyword(s):  
Memetic Algorithm ◽  
Smart Cities ◽  
Energy Aware

scholarly journals MOOC Dropout Prediction Using FWTS-CNN Model Based on Fused Feature Weighting and Time Series

IEEE Access ◽  
2020 ◽  
pp. 1-1
Author(s):  
Ya-Feng Zheng ◽  
Zhang-Hao Gao ◽  
Yi-Hang Wang ◽  
Qian Fu
Keyword(s):  
Time Series ◽  
Feature Weighting ◽  
Model Based
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