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MuDi-Stream: A multi density clustering algorithm for evolving data stream
2016
Journal of Network and Computer Applications
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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
2020
Artificial Intelligence Review
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2020 pre-print arXiv:2007.10781v1 fatcat:bbui32ece5ghdj32xbvytqiuja
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
2014
SIGMOD record
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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
2020
IEEE Access
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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
2021
Applied Sciences
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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
2016
International Journal of Advanced Computer Science and Applications
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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]
2015
arXiv
pre-print
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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
2019
IEEE Access
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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