Applied and Computational Engineering
- The Open Access Proceedings Series for Conferences
Proceedings of the 4th International Conference on Computing and Data Science (CONF-CDS 2022)
Series Vol. 2 , 22 March 2023
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Improving Performance Parameters of Clusters Using Density-Based Algorithm
* Author to whom correspondence should be addressed.
Abstract
With the advancement in technology, data generated by non-stationary in day-to-day life is massive, continuous and rapid. Many applications such as IoT, transaction systems, network sensors, video surveillance systems, and network intrusion detection systems generate a massive amount of real-time data. The data used in traditional data mining is static in nature, and it can be revised for processing and Analysis. While data in data stream mining is dynamic in nature and it never stops. Besides, the data generated may have a change imbibed in its characteristics over a long/short period of time which is called concept drift. So, analysing such data has huge inbuilt challenges that deal with the dynamism of the characteristics of data itself. This dynamic nature is because of fast and continuous changing data and its enormity. To overcome this limitation, we can use modified clustering techniques that could help us in proper data analysis. Clustering is an effective method used in data mining; but clustering data streams may add some additional challenges such as storage capacity, limited time, one pass and rate of arrival. Furthermore, data streams are fickle in nature and because of this behaviour it needs to be processed as and when it arrives. In addition to that, knowledge about the number of clusters like in K-means clustering is unknown. In view of these characteristics of the data stream, the information or the data generated in the data stream are non-deterministic. Such non-deterministic information contains noise points or outliers, so developing an effective clustering algorithm in a data stream is a crucial task. These methods can work with labelled data in data stream clustering, which has the potential to identify clusters of any shape and noise. The motivation for this research work is using the said algorithm to address and overcome the constraints of the data stream and to dig out the best knowledge from it.
Keywords
Concept Drift, Pruning, Massive Data, Micro-clusters, Clustering, Datastream, Arbitrary, Density-based
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Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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- Volume Title
- Proceedings of the 4th International Conference on Computing and Data Science (CONF-CDS 2022)
- ISBN (Print)
- 978-1-915371-19-5
- ISBN (Online)
- 978-1-915371-20-1
- Published Date
- 22 March 2023
- Series
- Applied and Computational Engineering
- ISSN (Print)
- 2755-2721
- ISSN (Online)
- 2755-273X
- DOI
- 10.54254/2755-2721/2/20220605
- Copyright
- 22 March 2023
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited