On the Hermite Series-Based Generalized Regression Neural Networks for Stream Data Mining

2019 ◽  
pp. 437-448
Author(s):  
Danuta Rutkowska ◽  
Leszek Rutkowski
Keyword(s):  
Data Mining ◽  
Neural Networks ◽  
Stream Data ◽  
Stream Data Mining ◽  
Generalized Regression Neural Networks ◽  
Generalized Regression ◽  
Hermite Series

Convergent Time-Varying Regression Models for Data Streams: Tracking Concept Drift by the Recursive Parzen-Based Generalized Regression Neural Networks

2018 ◽  
Vol 28 (02) ◽  
pp. 1750048 ◽  
Author(s):  
Piotr Duda ◽  
Maciej Jaworski ◽  
Leszek Rutkowski
Keyword(s):  
Data Mining ◽  
Neural Networks ◽  
Data Streams ◽  
Regression Models ◽  
Concept Drift ◽  
Real Life ◽  
Time Varying ◽  
Heuristic Approaches ◽  
Generalized Regression Neural Networks ◽  
Generalized Regression

One of the greatest challenges in data mining is related to processing and analysis of massive data streams. Contrary to traditional static data mining problems, data streams require that each element is processed only once, the amount of allocated memory is constant and the models incorporate changes of investigated streams. A vast majority of available methods have been developed for data stream classification and only a few of them attempted to solve regression problems, using various heuristic approaches. In this paper, we develop mathematically justified regression models working in a time-varying environment. More specifically, we study incremental versions of generalized regression neural networks, called IGRNNs, and we prove their tracking properties — weak (in probability) and strong (with probability one) convergence assuming various concept drift scenarios. First, we present the IGRNNs, based on the Parzen kernels, for modeling stationary systems under nonstationary noise. Next, we extend our approach to modeling time-varying systems under nonstationary noise. We present several types of concept drifts to be handled by our approach in such a way that weak and strong convergence holds under certain conditions. Finally, in the series of simulations, we compare our method with commonly used heuristic approaches, based on forgetting mechanism or sliding windows, to deal with concept drift. Finally, we apply our concept in a real life scenario solving the problem of currency exchange rates prediction.

scholarly journals Adaptive Trajectory Tracking Control for Quadrotors with Disturbances by Using Generalized Regression Neural Networks

2021 ◽  
Author(s):  
Ivan Lopez-Sanchez ◽  
Francisco Rossomando ◽  
Ricardo Pérez-Alcocer ◽  
Carlos Soria ◽  
Ricardo Carelli ◽  
...  
Keyword(s):  
Neural Networks ◽  
Trajectory Tracking ◽  
Tracking Control ◽  
Trajectory Tracking Control ◽  
Generalized Regression Neural Networks ◽  
Generalized Regression

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.

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