Improving Multivariate Data Streams Clustering
| Autor: | Luciana Alvim Santos Romani, Elaine Parros Machado de Sousa, Christian C. Bones |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Multivariate statistics
Computer science media_common.quotation_subject 02 engineering and technology STREAMS Machine learning computer.software_genre Clustering Fractal Data Streams 020204 information systems 0202 electrical engineering electronic engineering information engineering Data Mining Quality (business) Cluster analysis General Environmental Science media_common Data stream mining business.industry Aggregate (data warehouse) General Earth and Planetary Sciences 020201 artificial intelligence & image processing Data mining Artificial intelligence business computer |
| Zdroj: | ICCS |
| ISSN: | 1877-0509 |
| Popis: | Clustering data streams is an important task in data mining research. Recently, some algorithms have been proposed to cluster data streams as a whole, but just few of them deal with multivariate data streams. Even so, these algorithms merely aggregate the attributes without touching upon the correlation among them. In order to overcome this issue, we propose a new framework to cluster multivariate data streams based on their evolving behavior over time, exploring the correlations among their attributes by computing the fractal dimension. Experimental results with climate data streams show that the clusters’ quality and compactness can be improved compared to the competing method, leading to the thoughtfulness that attributes correlations cannot be put aside. In fact, the clusters’ compactness are 7 to 25 times better using our method. Our framework also proves to be an useful tool to assist meteorologists in understanding the climate behavior along a period of time. |
| Databáze: | OpenAIRE |
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