A novel approach for mining emerging patterns in data streams

2007 ◽  
Author(s):  
Hamad Alhammady
Keyword(s):  
Data Streams ◽  
Novel Approach ◽  
Emerging Patterns

Growing Neural Forest-Based Color Quantization Applied to RGB Images

2017 ◽  
Vol 7 (3) ◽  
pp. 13-25
Author(s):  
Jesús Benito-Picazo ◽  
Ezequiel López-Rubio ◽  
Enrique Domínguez
Keyword(s):  
Data Streams ◽  
Image Data ◽  
Color Quantization ◽  
Perceptual Distortion ◽  
Self Organized ◽  
Novel Approach ◽  
Image Quantization ◽  
Rgb Images ◽  
Storage Technologies ◽  
Future Work

Although last improvements in both physical storage technologies and image handling techniques have eased image managing processes, the large amount of information handled nowadays constantly demands more efficient ways to store and transmit image data streams. Among other alternatives for such purpose, the authors find color quantization, which consists of color indexing for minimal perceptual distortion image compression. In this context, artificial intelligence-based algorithms and more specifically, Artificial Neural Networks, have been consolidated as a powerful tool for unsupervised tasks, and therefore, for color quantization purposes. In this work, a novel approach to color quantization is presented based on the Growing Neural Forest (GNF), which is a Growing Neural Gas (GNG) variation where a set of trees is learnt instead of a general graph. Experimental results support the use of GNF for image quantization tasks where it overcomes other self-organized models including SOM, GHSOM and GNG. Future work will include more datasets and different competitive models to compare to.

scholarly journals FEPDS: A Proposal for the Extraction of Fuzzy Emerging Patterns in Data Streams

2020 ◽  
pp. 1-1
Author(s):  
Angel Miguel Garcia Vico ◽  
Cristobal Carmona ◽  
Pedro Gonzalez ◽  
Huseyin Seker ◽  
Maria Jose Del Jesus
Keyword(s):  
Data Streams ◽  
Emerging Patterns

ADAW: Age decay accuracy weighted ensemble method for drifting data stream mining

2021 ◽  
Vol 25 (5) ◽  
pp. 1131-1152
Author(s):  
Ritesh Srivastava ◽  
Veena Mittal
Keyword(s):  
Data Streams ◽  
Data Stream ◽  
Dynamic Environment ◽  
Ensemble Methods ◽  
Life Time ◽  
Novel Approach ◽  
Data Source ◽  
Methods Of Learning ◽  
Concept Drifts ◽  
Forgetting Mechanism

Dynamic environment data generators are very often in real-world that produce data streams. A data source of a dynamic environment generates data streams in which the underlying data distribution changes very frequently with respect to time and hence results in concept drifts. As compared to the stationary environment, learning in the dynamic environment is very difficult due to the presence of concept drifts. Learning in dynamic environment requires evolutionary and adaptive approaches to be accommodated with the learning algorithms. Ensemble methods are commonly used to build classifiers for learning in a dynamic environment. The ensemble methods of learning are generally described at three very crucial aspects, namely, the learning and testing method employed, result integration method and forgetting mechanism for old concepts. In this paper, we propose a novel approach called Age Decay Accuracy Weighted (ADAW) ensemble architecture for learning in concept drifting data streams. The ADAW method assigned weights to the component classifiers based on its accuracy and its remaining life-time in the ensemble is such a way that ensures maximum accuracy. We empirically evaluated ADAW on benchmark artificial drifting data stream generators and real datasets and compared its performance with ten well-known state-of-the-art existing methods. The experimental results show that ADAW outperforms over the existing methods.

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