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Applications of monitoring and tracing the evolution of clustering solutions in dynamic datasets.

Authors :
Atif, Muhammad
Shafiq, Muhammad
Leisch, Friedrich
Source :
Journal of Applied Statistics. Mar2023, Vol. 50 Issue 4, p1017-1035. 19p. 4 Diagrams, 8 Charts, 5 Graphs.
Publication Year :
2023

Abstract

The clustering approach is widely accepted as the most prominent unsupervised learning problem in data mining techniques. This procedure deals with the identification of notable structures in unlabeled datasets. In modern days clustering of dynamic data, streams play a vital role in policy-making, and researchers are paying particular attention to monitoring the evolution of clustering solutions over time. The data streams evolve continually, and different sources generate data items over time. The clustering solution over this stream is not stationary and changes with the influx of new data items. This paper presents a comprehensive study of algorithms related to tracing the evolution of clusters over time in cumulative datasets. To demonstrate the applications and significance of the tracing cluster evolution, we implement the MONIC algorithm in R-software. This article illustrates how the data segmentation of dynamic streams is done and shows the applications of monitoring changes in clustering solutions with the help of real-life published datasets. [ABSTRACT FROM AUTHOR]

Subjects

Subjects :
*DATA mining
*POLICY sciences

Details

Language :
English
ISSN :
02664763
Volume :
50
Issue :
4
Database :
Academic Search Index
Journal :
Journal of Applied Statistics
Publication Type :
Academic Journal
Accession number :
162355044
Full Text :
https://doi.org/10.1080/02664763.2021.2008882