Subspace outlier mining in large multimedia databases

Abstract. Increasingly large multimedia databases in life sciences, ecommerce, or monitoring applications cannot be browsed manually, but require automatic knowledge discovery in databases (KDD) techniques to detect novel and interesting patterns. Clustering, aims at grouping similar objects into clusters, separating dissimilar objects. Density-based clustering has been shown to detect arbitrarily shaped clusters even in noisy data bases. In high-dimensional data bases, meaningful clusters can no longer be detected due to the curse of dimensionality. Consequently, subspace clustering searches for clusters hidden in any subset of the set of dimensions. Clustering information is very useful for applications like fraud detection where outliers, i.e. objects which differ from all clusters, are searched. We propose a density-based subspace clustering model for outlier detection. We define outliers with respect to maximal and nonredundant subspace clusters. We demonstrate the quality of ou...

• Type: Conference
• Year: 2007
• Where: DAGSTUHL
• Authors: Ira Assent, Ralph Krieger, Emmanuel Müller, Thomas Seidl
• Keywords: DAGSTUHL 2007, Software Engineering, Subspace Clustering, Subspace Clustering Model, Subspace Clustering Searches