Name
Abstract | ||
|---|---|---|
In this paper, a novel autonomous data-driven clustering approach, called AD_clustering, is presented for live data streams processing. This newly proposed algorithm is a fully unsupervised approach and entirely based on the data samples and their ensemble properties, in the sense that there is no need for user-predefined or problem-specific assumptions and parameters, which is a problem most of the current clustering approaches suffer from. Moreover, the proposed approach automatically evolves its structure according to the experimentally observable streaming data and is able to recursively update its self-defined parameters using only the current data sample; meanwhile, it discards all the previously processed data samples. Experimental results based on benchmark datasets exhibit the higher performance of the proposed fully autonomous approach compared with the comparative approaches requiring user-and problem-specific parameters to be predefined. This new clustering algorithm is a promising tool for further applications in the field of real-time streaming data analytics. |
Year | Venue | Keywords |
|---|---|---|
2016 | 2016 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC) | fully unsupervised clustering, live data streams, ensemble properties, recursive update, streaming data analytics |
Field | DocType | ISSN |
Data mining,Fuzzy clustering,Canopy clustering algorithm,CURE data clustering algorithm,Clustering high-dimensional data,Data stream clustering,Correlation clustering,Computer science,Constrained clustering,Artificial intelligence,Cluster analysis,Machine learning | Conference | 1062-922X |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaowei Gu | 1 | 99 | 10.96 |
| Plamen P. Angelov | 2 | 473 | 27.83 |