A Support System for Clustering Data Streams with a Variable Number of Clusters

2016 ◽  
Vol 11 (2) ◽  
pp. 1-26 ◽  
Author(s):  
Jonathan de Andrade Silva ◽  
Eduardo Raul Hruschka
Keyword(s):  
Support System ◽  
Data Streams ◽  
Variable Number ◽  
Number Of Clusters ◽  
Clustering Data

An evolutionary algorithm for clustering data streams with a variable number of clusters

2017 ◽  
Vol 67 ◽  
pp. 228-238 ◽  
Author(s):  
Jonathan de Andrade Silva ◽  
Eduardo Raul Hruschka ◽  
João Gama
Keyword(s):  
Evolutionary Algorithm ◽  
Data Streams ◽  
Variable Number ◽  
Number Of Clusters ◽  
Clustering Data

scholarly journals PENERAPAN TEXT MINING UNTUK MELAKUKAN CLUSTERING DATA TWEET AKUN BLIBLI PADA MEDIA SOSIAL TWITTER MENGGUNAKAN K-MEANS CLUSTERING

2022 ◽  
Vol 10 (4) ◽  
pp. 583-593
Author(s):  
Syiva Multi Fani ◽  
Rukun Santoso ◽  
Suparti Suparti
Keyword(s):  
Social Media ◽  
Text Mining ◽  
Virtual Networks ◽  
Number Of Clusters ◽  
Silhouette Coefficient ◽  
Twitter Account ◽  
Computer Based ◽  
Twitter Users ◽  
Clustering Data ◽  
Coefficient Method

Social media is computer-based technology that facilitates the sharing of ideas, thoughts, and information through the building of virtual networks and communities. Twitter is one of the most popular social media in Indonesia which has 78 million users. Businesses rely heavily on Twitter for advertising. Businesses can use these types of tweet content as a means of advertising to Twitter users by Knowing the types of tweet content that are mostly retweeted by their followers . In this study, the application of Text Mining to perform clustering using the K-means clustering method with the best number of clusters obtained from the Silhouette Coefficient method on the @bliblidotcom Twitter tweet data to determine the types of tweet content that are mostly retweeted by @bliblidotcom followers. Tweets with the most retweets and favorites are discount offers and flash sales, so Blibli Indonesia could use this kind of tweet to conduct advertising on social media Twitter because the prize quiz tweets are liked by the @bliblidotcom Twitter account followers.

The Algorithm of Quadratic Junction Neural Network

2013 ◽  
Vol 462-463 ◽  
pp. 438-442
Author(s):  
Ming Gu
Keyword(s):  
Neural Network ◽  
Clustering Algorithm ◽  
Artificial Data ◽  
Data Set ◽  
Number Of Clusters ◽  
Learning Rules ◽  
The Neural Network ◽  
Hierarchical Clustering Algorithm ◽  
Clustering Data ◽  
Structure Properties

Neural network with quadratic junction was described. Structure, properties and unsupervised learning rules of the neural network were discussed. An ART-based hierarchical clustering algorithm using this kind of neural networks was suggested. The algorithm can determine the number of clusters and clustering data. A 2-D artificial data set is used to illustrate and compare the effectiveness of the proposed algorithm and K-means algorithm.

Requirements for clustering data streams

2002 ◽  
Vol 3 (2) ◽  
pp. 23-27 ◽  
Author(s):  
Daniel Barbará
Keyword(s):  
Data Streams ◽  
Clustering Data
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