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Data Stream Classification by Dynamic Incremental Semi-Supervised Fuzzy Clustering.
- Source :
-
International Journal on Artificial Intelligence Tools . Dec2019, Vol. 28 Issue 8, pN.PAG-N.PAG. 26p. - Publication Year :
- 2019
-
Abstract
- A data stream classification method called DISSFCM (Dynamic Incremental Semi-Supervised FCM) is presented, which is based on an incremental semi-supervised fuzzy clustering algorithm. The method assumes that partially labeled data belonging to different classes are continuously available during time in form of chunks. Each chunk is processed by semi-supervised fuzzy clustering leading to a cluster-based classification model. The proposed DISSFCM is capable of dynamically adapting the number of clusters to data streams, by splitting low-quality clusters so as to improve classification quality. Experimental results on both synthetic and real-world data show the effectiveness of the proposed method in data stream classification. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 02182130
- Volume :
- 28
- Issue :
- 8
- Database :
- Academic Search Index
- Journal :
- International Journal on Artificial Intelligence Tools
- Publication Type :
- Academic Journal
- Accession number :
- 140053832
- Full Text :
- https://doi.org/10.1142/S0218213019600091