A Novel Preformulation Tool to Group Microcrystalline Celluloses Using Artificial Neural Network and Data Clustering

Various types of derivative information have been increasing exponentially, based on mobile devices and social networking sites (SNSs), and the information technologies utilizing them have also been developing rapidly. Technologies to classify and analyze such information are as important as data generation. This study concentrates on data clustering through principal component analysis and K-means algorithms to analyze and classify user data efficiently. We propose a technique of changing the cluster choice before cluster processing in the existing K-means practice into a variable cluster choice through principal component analysis, and expanding the scope of data clustering. The technique also applies an artificial neural network learning model for user recommendation and prediction from the clustered data. The proposed processing model for predicted data generated results that improved the existing artificial neural network–based data clustering and learning model by approximately 9.25%.

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Using artificial neural network-self-organising map for data clustering of marine engine condition monitoring applications

Ships and Offshore Structures ◽

10.1080/17445302.2018.1443694 ◽

2018 ◽

Vol 13 (6) ◽

pp. 649-656 ◽

Cited By ~ 19

Author(s):

Yiannis Raptodimos ◽

Iraklis Lazakis

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Condition Monitoring ◽

Data Clustering ◽

Marine Engine ◽

Monitoring Applications ◽

Artificial Neural ◽

Self Organising Map ◽

Engine Condition

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A dynamic size artificial neural network for online data clustering with a new outlier handling technique

The 16th CSI International Symposium on Artificial Intelligence and Signal Processing (AISP 2012) ◽

10.1109/aisp.2012.6313767 ◽

2012 ◽

Author(s):

Amir Mehrafsa ◽

Ghader Karimian ◽

Ahmad Ghanbari

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Data Clustering ◽

Online Data ◽

Handling Technique ◽

Artificial Neural

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Artificial Neural Network ensemble modeling with conjunctive data clustering for water quality prediction in rivers

Journal of Hydro-environment Research ◽

10.1016/j.jher.2014.09.006 ◽

2015 ◽

Vol 9 (3) ◽

pp. 325-339 ◽

Cited By ~ 34

Author(s):

Sung Eun Kim ◽

Il Won Seo

Keyword(s):

Neural Network ◽

Water Quality ◽

Artificial Neural Network ◽

Data Clustering ◽

Quality Prediction ◽

Ensemble Modeling ◽

Neural Network Ensemble ◽

Water Quality Prediction ◽

Artificial Neural ◽

Artificial Neural Network Ensemble

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Prediction Data Processing Scheme using an Artificial Neural Network and Data Clustering for Big Data

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v6i1.9334 ◽

2016 ◽

Vol 6 (1) ◽

pp. 330 ◽

Cited By ~ 3

Author(s):

Se-Hoon Jung ◽

Jong-Chan Kim ◽

Chun-Bo Sim

Keyword(s):

Neural Network ◽

Principal Component Analysis ◽

Artificial Neural Network ◽

Data Clustering ◽

Principal Component ◽

Component Analysis ◽

Learning Model ◽

Data Generation ◽

Processing Scheme ◽

Artificial Neural

Various types of derivative information have been increasing exponentially, based on mobile devices and social networking sites (SNSs), and the information technologies utilizing them have also been developing rapidly. Technologies to classify and analyze such information are as important as data generation. This study concentrates on data clustering through principal component analysis and K-means algorithms to analyze and classify user data efficiently. We propose a technique of changing the cluster choice before cluster processing in the existing K-means practice into a variable cluster choice through principal component analysis, and expanding the scope of data clustering. The technique also applies an artificial neural network learning model for user recommendation and prediction from the clustered data. The proposed processing model for predicted data generated results that improved the existing artificial neural network–based data clustering and learning model by approximately 9.25%.

Download Full-text