Discovering Local Outlier Based on Rough Clustering

Discovering Local Outlier Based on Rough Clustering

The density at a data point is defined based on kernel function. And we introduce weight to refine rough k-means algorithm. Then we construct the formula for calculating local outlier score based on the clusters generated by the refined rough k-means algorithm. We use a synthetic data set and a real...

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Bibliographische Detailangaben
1. Verfasser: Hongjuan Mi
Format: Tagungsbericht
Sprache:eng
Schlagworte:
Algorithm design and analysis
Approximation algorithms
Approximation methods
Clustering algorithms
Data mining
Kernel
Rough sets
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Beschreibung
Zusammenfassung:The density at a data point is defined based on kernel function. And we introduce weight to refine rough k-means algorithm. Then we construct the formula for calculating local outlier score based on the clusters generated by the refined rough k-means algorithm. We use a synthetic data set and a real-world data set to verify that the new technique for local outliers detection is not only accurate but also efficient.
DOI:10.1109/ISA.2011.5873272