Detecting IoT Botnet Formation using Data Stream Clustering Algorithms

2020 ◽  
Author(s):  
Gabriel Arimatéa ◽  
Admilson Ribeiro
Keyword(s):  
Data Stream ◽  
Clustering Algorithms ◽  
Stream Clustering ◽  
Data Stream Clustering ◽  
Using Data

Twitter spammer detection using data stream clustering

2014 ◽  
Vol 260 ◽  
pp. 64-73 ◽  
Author(s):  
Zachary Miller ◽  
Brian Dickinson ◽  
William Deitrick ◽  
Wei Hu ◽  
Alex Hai Wang
Keyword(s):  
Data Stream ◽  
Stream Clustering ◽  
Data Stream Clustering ◽  
Using Data

scholarly journals EvolveCluster: an evolutionary clustering algorithm for streaming data

2021 ◽  
Author(s):  
Christian Nordahl ◽  
Veselka Boeva ◽  
Håkan Grahn ◽  
Marie Persson Netz
Keyword(s):  
Data Streams ◽  
Data Stream ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Streaming Data ◽  
Evolutionary Clustering ◽  
Stream Clustering ◽  
The Past ◽  
Data Stream Clustering ◽  
Evolving Data

AbstractData has become an integral part of our society in the past years, arriving faster and in larger quantities than before. Traditional clustering algorithms rely on the availability of entire datasets to model them correctly and efficiently. Such requirements are not possible in the data stream clustering scenario, where data arrives and needs to be analyzed continuously. This paper proposes a novel evolutionary clustering algorithm, entitled EvolveCluster, capable of modeling evolving data streams. We compare EvolveCluster against two other evolutionary clustering algorithms, PivotBiCluster and Split-Merge Evolutionary Clustering, by conducting experiments on three different datasets. Furthermore, we perform additional experiments on EvolveCluster to further evaluate its capabilities on clustering evolving data streams. Our results show that EvolveCluster manages to capture evolving data stream behaviors and adapts accordingly.

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.

scholarly journals Data Stream Clustering Algorithms: Challenges and Future Directions

2019 ◽  
Vol 8 (11) ◽  
pp. 3676-3681
Keyword(s):  
Data Stream ◽  
Weather Forecasting ◽  
Clustering Algorithms ◽  
Research Area ◽  
Streaming Data ◽  
Clustering Methods ◽  
Stream Clustering ◽  
Data Stream Clustering ◽  
Active Research ◽  
Financial Transactions

In the fast growing world applications are generating data in enormous volumes called data streams. Data stream is imaginably large, continual, rapid flow of information and in data mining the important tool is called clustering, hence data stream clustering (DSC) can be said as active research area. Recent attention of data stream clustering is through the applications that contain large amounts of streaming data. Data stream clustering is used in many areas such as weather forecasting, financial transactions, website analysis, sensor network monitoring, e-business, telephone records and telecommunications. In case of data stream clustering most popularly used heuristic is K-means and other algorithms like K-medoids and the popular BIRCH are developed. The aim of the abstract is to review the developments and trends of data stream clustering methods and analyze typical DSC algorithms proposed in recent years, such as BIRCH, STREAM, DSTREAM and some more algorithms.

An evaluation of data stream clustering algorithms

2018 ◽  
Vol 11 (4) ◽  
pp. 167-187 ◽  
Author(s):  
Stratos Mansalis ◽  
Eirini Ntoutsi ◽  
Nikos Pelekis ◽  
Yannis Theodoridis
Keyword(s):  
Data Stream ◽  
Clustering Algorithms ◽  
Stream Clustering ◽  
Data Stream Clustering ◽  
Evaluation Of Data

Research on data stream clustering algorithms

2013 ◽  
Vol 43 (4) ◽  
pp. 593-600 ◽  
Author(s):  
Shifei Ding ◽  
Fulin Wu ◽  
Jun Qian ◽  
Hongjie Jia ◽  
Fengxiang Jin
Keyword(s):  
Data Stream ◽  
Clustering Algorithms ◽  
Stream Clustering ◽  
Data Stream Clustering
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