A Weightless Neural Network-Based Approach for Stream Data Clustering

2012 ◽  
pp. 328-335 ◽  
Author(s):  
Douglas Cardoso ◽  
Massimo De Gregorio ◽  
Priscila Lima ◽  
João Gama ◽  
Felipe França
Keyword(s):  
Neural Network ◽  
Data Clustering ◽  
Stream Data

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

2016 ◽  
Vol 6 (1) ◽  
pp. 330 ◽  
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%.

scholarly journals A review on data clustering using spiking neural network (SNN) models

2019 ◽  
Vol 15 (3) ◽  
pp. 1392
Author(s):  
Siti Aisyah Mohamed ◽  
Muhaini Othman ◽  
Mohd Hafizul Afifi
Keyword(s):  
Neural Network ◽  
Data Clustering ◽  
Network Clustering ◽  
Complex Data ◽  
Spiking Neural Network ◽  
Clustering Methods ◽  
Clustering Method ◽  
Static Data ◽  
Clustering Approach ◽  
Clustering Problems

The evolution of Artificial Neural Network recently gives researchers an interest to explore deep learning evolved by Spiking Neural Network clustering methods. Spiking Neural Network (SNN) models captured neuronal behaviour more precisely than a traditional neural network as it contains the theory of time into their functioning model [1]. The aim of this paper is to reviewed studies that are related to clustering problems employing Spiking Neural Networks models. Even though there are many algorithms used to solve clustering problems, most of the methods are only suitable for static data and fixed windows of time series. Hence, there is a need to analyse complex data type, the potential for improvement is encouraged. Therefore, this paper summarized the significant result obtains by implying SNN models in different clustering approach. Thus, the findings of this paper could demonstrate the purpose of clustering method using SNN for the fellow researchers from various disciplines to discover and understand complex data.

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