Concept Drift Detection in Data Streams

2021 ◽  
pp. 31-55
Author(s):  
Sayan Putatunda
Keyword(s):  
Data Streams ◽  
Concept Drift ◽  
Concept Drift Detection

scholarly journals Concept Drift Adaptation Techniques in Distributed Environment for Real-World Data Streams

Smart Cities ◽  
2021 ◽  
Vol 4 (1) ◽  
pp. 349-371
Author(s):  
Hassan Mehmood ◽  
Panos Kostakos ◽  
Marta Cortes ◽  
Theodoros Anagnostopoulos ◽  
Susanna Pirttikangas ◽  
...  
Keyword(s):  
Real World ◽  
Data Streams ◽  
Smart City ◽  
Smart Cities ◽  
Concept Drift ◽  
Distributed Environment ◽  
Real World Data ◽  
Unique Challenge ◽  
World Data ◽  
Concept Drift Detection

Real-world data streams pose a unique challenge to the implementation of machine learning (ML) models and data analysis. A notable problem that has been introduced by the growth of Internet of Things (IoT) deployments across the smart city ecosystem is that the statistical properties of data streams can change over time, resulting in poor prediction performance and ineffective decisions. While concept drift detection methods aim to patch this problem, emerging communication and sensing technologies are generating a massive amount of data, requiring distributed environments to perform computation tasks across smart city administrative domains. In this article, we implement and test a number of state-of-the-art active concept drift detection algorithms for time series analysis within a distributed environment. We use real-world data streams and provide critical analysis of results retrieved. The challenges of implementing concept drift adaptation algorithms, along with their applications in smart cities, are also discussed.

On learning guarantees to unsupervised concept drift detection on data streams

2019 ◽  
Vol 117 ◽  
pp. 90-102 ◽  
Author(s):  
Rodrigo F. de Mello ◽  
Yule Vaz ◽  
Carlos H. Grossi ◽  
Albert Bifet
Keyword(s):  
Data Streams ◽  
Concept Drift ◽  
Concept Drift Detection

A novel concept drift detection method in data streams using ensemble classifiers

2016 ◽  
Vol 20 (6) ◽  
pp. 1329-1350 ◽  
Author(s):  
Mahdie Dehghan ◽  
Hamid Beigy ◽  
Poorya ZareMoodi
Keyword(s):  
Data Streams ◽  
Detection Method ◽  
Concept Drift ◽  
Ensemble Classifiers ◽  
Concept Drift Detection ◽  
Novel Concept

Nacre: Proactive Recurrent Concept Drift Detection in Data Streams

2021 ◽  
Author(s):  
Ocean Wu ◽  
Yun Sing Koh ◽  
Gillian Dobbie ◽  
Thomas Lacombe
Keyword(s):  
Data Streams ◽  
Concept Drift ◽  
Concept Drift Detection

scholarly journals Concept Drift Detection in Data Stream Clustering and its Application on Weather Data

2020 ◽  
Vol 11 (1) ◽  
pp. 67-85 ◽  
Author(s):  
Namitha K. ◽  
Santhosh Kumar G.
Keyword(s):  
Data Streams ◽  
Data Stream ◽  
Weather Forecasting ◽  
Concept Drift ◽  
Clustering Algorithms ◽  
Weather Data ◽  
Stream Clustering ◽  
Cluster Evolution ◽  
Data Stream Clustering ◽  
Concept Drift Detection

This article presents a stream mining framework to cluster the data stream and monitor its evolution. Even though concept drift is expected to be present in data streams, explicit drift detection is rarely done in stream clustering algorithms. The proposed framework is capable of explicit concept drift detection and cluster evolution analysis. Concept drift is caused by the changes in data distribution over time. Relationship between concept drift and the occurrence of physical events has been studied by applying the framework on the weather data stream. Experiments led to the conclusion that the concept drift accompanied by a change in the number of clusters indicates a significant weather event. This kind of online monitoring and its results can be utilized in weather forecasting systems in various ways. Weather data streams produced by automatic weather stations (AWS) are used to conduct this study.

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