A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mining high-speed data streams
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
On Clustering Validation Techniques
Journal of Intelligent Information Systems
Online clustering of parallel data streams
Data & Knowledge Engineering
Adaptive Clustering for Multiple Evolving Streams
IEEE Transactions on Knowledge and Data Engineering
Robust Division in Clustering of Streaming Time Series
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
ICDM'10 Proceedings of the 10th industrial conference on Advances in data mining: applications and theoretical aspects
Dynamic agglomerative-divisive clustering of clickthrough data for collaborative web search
DASFAA'10 Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part I
| Hi-index | 0.00 |
The Online Divisive-Agglomerative Clustering (ODAC) is an incremental approach for clustering streaming time series using a hierarchical procedure over time. It constructs a tree-like hierarchy of clusters of streams, using a top-down strategy based on the correlation between streams. The system also possesses an agglomerative phase to enhance a dynamic behavior capable of structural change detection. However, the split decision used in the algorithm focus on the crisp boundary between two groups, which implies a high risk since it has to decide based on only a small subset of the entire data. In this work we propose a semi-fuzzy approach to the assignment of variables to newly created clusters, for a better trade-off between validity and performance. Experimental work supports the benefits of our approach.