Simultaneous Clustering and Outlier Detection using Dominant sets
| Autor: | Marcello Pelillo, Yonatan Tariku Tesfaye, Eyasu Zemene, Andrea Prati |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Fuzzy clustering
Settore INF/01 - Informatica Single-linkage clustering Correlation clustering Constrained clustering 02 engineering and technology computer.software_genre ComputingMethodologies_PATTERNRECOGNITION CURE data clustering algorithm 020204 information systems Outlier 0202 electrical engineering electronic engineering information engineering Canopy clustering algorithm 020201 artificial intelligence & image processing Data mining Cluster analysis computer Mathematics |
| Zdroj: | ICPR |
| Popis: | We present a unified approach for simultaneous clustering and outlier detection in data. We utilize some properties of a family of quadratic optimization problems related to dominant sets, a well-known graph-theoretic notion of a cluster which generalizes the concept of a maximal clique to edge-weighted graphs. Unlike most (all) of the previous techniques, in our framework the number of clusters arises intuitively and outliers are obliterated automatically. The resulting algorithm discovers both parameters from the data. Experiments on real and on large scale synthetic dataset demonstrate the effectiveness of our approach and the utility of carrying out both clustering and outlier detection in a concurrent manner. |
| Databáze: | OpenAIRE |
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