A novel Fibonacci windows model for finding emerging patterns over online data stream

2015 ◽  
Author(s):  
Tubagus M. Akhriza ◽  
Yinghua Ma ◽  
Jianhua Li
Keyword(s):  
Data Stream ◽  
Online Data ◽  
Emerging Patterns

scholarly journals An Ensemble of Adaptive Neuro-Fuzzy Kohonen Networks for Online Data Stream Fuzzy Clustering

2016 ◽  
Vol 8 (5) ◽  
pp. 12-18 ◽  
Author(s):  
Zhengbing Hu ◽  
◽  
Yevgeniy V. Bodyanskiy ◽  
Oleksii K. Tyshchenko ◽  
Olena O. Boiko
Keyword(s):  
Fuzzy Clustering ◽  
Data Stream ◽  
Online Data ◽  
Neuro Fuzzy ◽  
Kohonen Networks

ESA-Stream: Efficient Self-Adaptive Online Data Stream Clustering

2020 ◽  
pp. 1-1
Author(s):  
Yanni Li ◽  
Hui Li ◽  
Zhi Wang ◽  
Bing Liu ◽  
Jiangtao Cui ◽  
...  
Keyword(s):  
Data Stream ◽  
Online Data ◽  
Stream Clustering ◽  
Data Stream Clustering ◽  
Self Adaptive

scholarly journals An ensemble based on neural networks with random weights for online data stream regression

2019 ◽  
Vol 24 (13) ◽  
pp. 9835-9855 ◽  
Author(s):  
Ricardo de Almeida ◽  
Yee Mey Goh ◽  
Radmehr Monfared ◽  
Maria Teresinha Arns Steiner ◽  
Andrew West
Keyword(s):  
Data Stream ◽  
Prediction Accuracy ◽  
Concept Drift ◽  
Learning Algorithms ◽  
Data Distribution ◽  
Machine Learning Algorithms ◽  
Computational Time ◽  
Online Data ◽  
Data Prediction ◽  
Random Weights

Abstract Most information sources in the current technological world are generating data sequentially and rapidly, in the form of data streams. The evolving nature of processes may often cause changes in data distribution, also known as concept drift, which is difficult to detect and causes loss of accuracy in supervised learning algorithms. As a consequence, online machine learning algorithms that are able to update actively according to possible changes in the data distribution are required. Although many strategies have been developed to tackle this problem, most of them are designed for classification problems. Therefore, in the domain of regression problems, there is a need for the development of accurate algorithms with dynamic updating mechanisms that can operate in a computational time compatible with today’s demanding market. In this article, the authors propose a new bagging ensemble approach based on neural network with random weights for online data stream regression. The proposed method improves the data prediction accuracy as well as minimises the required computational time compared to a recent algorithm for online data stream regression from literature. The experiments are carried out using four synthetic datasets to evaluate the algorithm’s response to concept drift, along with four benchmark datasets from different industries. The results indicate improvement in data prediction accuracy, effectiveness in handling concept drift, and much faster updating times compared to the existing available approach. Additionally, the use of design of experiments as an effective tool for hyperparameter tuning is demonstrated.

scholarly journals Critical evaluation of classifiers in data stream mining

2018 ◽  
Vol 7 (4) ◽  
pp. 2166
Author(s):  
Lalit Agrawal ◽  
Dattatraya Adane
Keyword(s):  
Data Stream ◽  
Forest Cover ◽  
Critical Evaluation ◽  
High Volume ◽  
Data Stream Mining ◽  
Volume Data ◽  
Stream Mining ◽  
Full Data ◽  
Online Data ◽  
Benchmark Datasets

Over past decade there has been a significant increase in the volume of online data. Extracting meaningful knowledge from this high volume data is considered as important aspect of research. It is very difficult to completely store full data, because of its perpetual nature. Therefore, analysis is needed while the “data is moving”. This moving data is known as data stream and analyzing it without storing it completely is termed as data stream mining. In recent years, many new techniques have been proposed to overcome the challenges of data stream mining. In this paper, we review the operation of popular streaming algorithms highlighting their strength and weaknesses. We also evaluate the classifiers used in these algorithms against two popular benchmark datasets namely (a) forest cover (forest) and (b) german credit available at UCI repository. Finally, we present our critical observation and draw conclusions on the basis of our analysis.  

EFFICIENTLY MINING RECENT FREQUENT PATTERNS OVER ONLINE TRANSACTIONAL DATA STREAMS

2009 ◽  
Vol 19 (05) ◽  
pp. 707-725 ◽  
Author(s):  
HUI CHEN
Keyword(s):  
Data Mining ◽  
Data Stream ◽  
Frequent Patterns ◽  
Stream Data ◽  
Data Stream Management ◽  
Online Data ◽  
Stream Data Mining ◽  
Network Traffic Analysis ◽  
Stream Management ◽  
Performance Results

Recent emerging applications, such as network traffic analysis, web click stream mining, power consumption measurement, sensor network data analysis, and dynamic tracing of stock fluctuation, call for study of a new kind of data, stream data. Many data stream management systems, prototype systems and software components have been developed to manage the streams or extract knowledge from stream data. Mining frequent patterns is a foundational job for the methods of data mining and knowledge discovery. This paper proposes an algorithm for mining the recent frequent patterns over an online data stream. This method uses RFP-tree to store compactly the recent frequent patterns of a stream. The content of each transaction is incrementally updated into the pattern tree upon its arrival by scanning the stream only once. Moreover, the strategy of conservative computation and time decaying model are used to ensure the correctness of the mining results. Finally, the performance results of extensive simulation show that our work can reduce the average processing time of stream data element and it is superior to other analogous algorithms.

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