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MuDi-Stream: A multi density clustering algorithm for evolving data stream

Amineh Amini, Hadi Saboohi, Tutut Herawan, Teh Ying Wah
2016 Journal of Network and Computer Applications  
Density-based method has emerged as a worthwhile class for clustering data streams. Recently, a number of density-based algorithms have been developed for clustering data streams.  ...  In the online phase, it keeps summary information about evolving multi-density data stream in the form of core mini-clusters.  ...  MuDi-Stream: A multi density clustering algorithm for evolving data stream.  ... 
doi:10.1016/j.jnca.2014.11.007 fatcat:bialc46lurbntknmisl6rhixsi

Data stream clustering: a review

Alaettin Zubaroğlu, Volkan Atalay
2020 Artificial Intelligence Review  
A comparison of these algorithms is given along with still open problems. We indicate popular data stream repositories and datasets, stream processing tools and platforms.  ...  We comprehensively review recent data stream clustering algorithms and analyze them in terms of the base clustering technique, computational complexity and clustering accuracy.  ...  MuDi-Stream MuDi-Stream customizes the density threshold for each cluster and overcomes the problem of multi density clusters.  ... 
doi:10.1007/s10462-020-09874-x fatcat:27fq6ccbrzb4xpfoaatic3rlim

A Panorama of Imminent Doctoral Research in Data Mining

Aparna S. Varde, Nikolaj Tatti
2014 SIGMOD record  
IEEE ICDM hosts such a PhD forum for doctoral students with a data mining focus. This article describes the content of the work presented at the ICDM 2013 PhD forum.  ...  As databases head towards data streams, discovering knowledge from the data poses challenges.  ...  Jilles Vreeken for giving an encouraging keynote talk. We express our gratitude towards all the PC members for reviewing papers.  ... 
doi:10.1145/2694428.2694442 fatcat:suspacylifdqjpnxvt6zfibz2u

Online Clustering of Evolving Data Streams using a Density Grid-based Method

Mustafa Tareq, Elankovan A. Sundararajan, Masnizah Mohd, Nor Samsiah Sani
2020 IEEE Access  
The CEDGM is a new algorithm for discovering the clusters of evolving data streams in multi-density environments.  ...  For IoT streams, a density-based clustering algorithm that can be used is hybrid density-based clustering for data streams (HDC-Stream) [17] .  ... 
doi:10.1109/access.2020.3021684 fatcat:f7x4jn2qo5dpxkc7fioryfw4jq

A Review of Machine Learning and Deep Learning Techniques for Anomaly Detection in IoT Data

Redhwan Al-amri, Raja Kumar Murugesan, Mustafa Man, Alaa Fareed Abdulateef, Mohammed A. Al-Sharafi, Ammar Ahmed Alkahtani
2021 Applied Sciences  
Research challenges related to data evolving, feature-evolving, windowing, ensemble approaches, nature of input data, data complexity and noise, parameters selection, data visualizations, heterogeneity  ...  However, there is a lack of comprehensive studies that discuss all the aspects of IoT data processing.  ...  The online phase maintains summary information on the evolving multi-density data stream into core mini clusters.  ... 
doi:10.3390/app11125320 fatcat:cjbzetn3xbb3tlm7lebaglujei

A Novel High Dimensional and High Speed Data Streams Algorithm: HSDStream

Irshad Ahmed, Irfan Ahmed, Waseem Shahzad
2016 International Journal of Advanced Computer Science and Applications  
This paper presents a novel high speed clustering scheme for high-dimensional data stream.  ...  High dimensional stream data is inherently more complex when used for clustering because the evolving nature of the stream data and high dimensionality make it non-trivial.  ...  MuDi-Stream [20] is a hybrid grid-based multi-density clustering algorithm with online-offline phases.  ... 
doi:10.14569/ijacsa.2016.070952 fatcat:jz2evpcm2ncbhl5n4uobp2zzem

Scaling up for high dimensional and high speed data streams: HSDStream [article]

Irshad Ahmed, Irfan Ahmed, Waseem Shahzad
2015 arXiv   pre-print
This paper presents a novel high speed clustering scheme for high dimensional data streams.  ...  High dimensional stream data is inherently more complex when used for clustering because the evolving nature of the stream data and high dimensionality make it non-trivial.  ...  MuDi-Stream [20] is a hybrid grid-based multi-density clustering algorithm with online-offline phases.  ... 
arXiv:1510.03375v1 fatcat:s3ewalkrajfmtdug4dlfpwsgci

A Novel Streaming Data Clustering Algorithm based on Fitness Proportionate Sharing

Xuyang Yan, Mohammad Razeghi-Jahromi, Abdollah Homaifar, Berat A. Erol, Abenezer Girma, Edward Tunstel
2019 IEEE Access  
To capture the dynamic characteristics of streaming data, a recursive formula for the lower bound of the density function is derived, and a summary of historical data is established for the proposed algorithm  ...  It introduces a density-based objective function and adopts the fitness proportionate sharing strategy to perform a more effective search for the cluster centers.  ...  MuDi-Stream [38] is designed to enhance the cluster analysis of data streams with multi-density clusters.  ... 
doi:10.1109/access.2019.2922162 fatcat:zk2dlo6buvejhb5u7moq53qrg4