scholarly journals Enabling Real-Time Business Intelligence by Stream Data Mining

10.5772/13123 ◽  
2011 ◽  
Author(s):  
Simon Fong ◽  
Yang Hang
Keyword(s):  
Data Mining ◽  
Real Time ◽  
Business Intelligence ◽  
Stream Data ◽  
Stream Data Mining

Real-time stream data mining based on CanTree and Gtree

2016 ◽  
Vol 367-368 ◽  
pp. 512-528 ◽  
Author(s):  
Jaein Kim ◽  
Buhyun Hwang
Keyword(s):  
Data Mining ◽  
Real Time ◽  
Stream Data ◽  
Stream Data Mining

A Clustering Algorithm in Stream Data Using Strong Coreset

2021 ◽  
Author(s):  
Manmohan Singh ◽  
Rajendra Pamula ◽  
Alok Kumar
Keyword(s):  
Data Mining ◽  
Clustering Algorithm ◽  
Local Optimum ◽  
Reduction Algorithm ◽  
Stream Data ◽  
Stream Data Mining ◽  
Clustering Approach ◽  
Approximation Guarantee ◽  
Competitive Algorithms ◽  
Learning Data

There are various applications of clustering in the fields of machine learning, data mining, data compression along with pattern recognition. The existent techniques like the Llyods algorithm (sometimes called k-means) were affected by the issue of the algorithm which converges to a local optimum along with no approximation guarantee. For overcoming these shortcomings, an efficient k-means clustering approach is offered by this paper for stream data mining. Coreset is a popular and fundamental concept for k-means clustering in stream data. In each step, reduction determines a coreset of inputs, and represents the error, where P represents number of input points according to nested property of coreset. Hence, a bit reduction in error of final coreset gets n times more accurate. Therefore, this motivated the author to propose a new coreset-reduction algorithm. The proposed algorithm executed on the Covertype dataset, Spambase dataset, Census 1990 dataset, Bigcross dataset, and Tower dataset. Our algorithm outperforms with competitive algorithms like Streamkm[Formula: see text], BICO (BIRCH meets Coresets for k-means clustering), and BIRCH (Balance Iterative Reducing and Clustering using Hierarchies.

Evolving clustering algorithm based on mixture of typicalities for stream data mining

2020 ◽  
Vol 106 ◽  
pp. 672-684 ◽  
Author(s):  
José Maia ◽  
Carlos Alberto Severiano ◽  
Frederico Gadelha Guimarães ◽  
Cristiano Leite de Castro ◽  
André Paim Lemos ◽  
...  
Keyword(s):  
Data Mining ◽  
Clustering Algorithm ◽  
Stream Data ◽  
Stream Data Mining
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