Comparative Study of Different Classification Algorithms for Stream Data Mining Using MOA

2018 ◽  
Vol 6 (11) ◽  
pp. 614-616
Author(s):  
Ashish P. Joshi ◽  
Biraj V. Patel
Keyword(s):  
Data Mining ◽  
Comparative Study ◽  
Classification Algorithms ◽  
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

Comparative Analysis of Drift Detection Based Adaptive Ensemble Model with Different Drift Detection Techniques

2021 ◽  
Vol 23 (06) ◽  
pp. 49-55
Author(s):  
Sanjeev Kumar ◽  
◽  
Ravendra Singh ◽  
Keyword(s):  
Data Mining ◽  
Comparative Analysis ◽  
False Positive ◽  
Opinion Mining ◽  
Concept Drift ◽  
Ensemble Classifier ◽  
Stream Data ◽  
Detection Techniques ◽  
Stream Data Mining ◽  
Detection Algorithms

Stream data mining is a popular research area these days. The concept drift detection and drift handling are the biggest challenges of stream data mining. Several drift detection algorithms have been developed which can accurately detect various drifts but have the problem of false-positive drift detection. The false-positive drift detection leads to the performance degradation of the classifier because of unnecessary training in between analyses. Classifier ensemble has shown its efficiency for drift detection, drift handling, and classification. But the ensemble classifiers could not detect the exact position of drift occurrence, so it has to update itself at some fixed interval, which leads to an unnecessary computational burden on the system. Combining the drift detection algorithm with an ensemble classifier can improve the performance and also solve the problems of false-positive drift detection and unnecessary updating of the ensemble classifier. In this paper, a model is proposed that creates a weighted adaptive ensemble classifier by updating it only when a drift detection signal is given by the used drift detection method. The proposed model is evaluated on text-based stream data for sentiment analysis and opinion mining with multiple drift detection algorithms and with multiple classification algorithms as base classifiers for the ensemble. A comparative analysis has been done, and the results have shown the efficiency of the proposed models.

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