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—Outlier mining is a major task in data analysis. Outliers are objects that highly deviate from regular objects in their local neighborhood. Density-based outlier ranking methods score each object based on its degree of deviation. In many applications, these ranking methods degenerate to random list-ings due to low contrast between outliers and regular(More)
—Subspace clustering aims at detecting clusters in any subspace projection of a high dimensional space. As the number of possible subspace projections is exponential in the number of dimensions, the result is often tremendously large. Recent approaches fail to reduce results to relevant subspace clusters. Their results are typically highly redundant, i.e.(More)
— Outlier analysis is an important data mining task that aims to detect unexpected, rare, and suspicious objects. Outlier ranking enables enhanced outlier exploration, which assists the user-driven outlier analysis. It overcomes the binary detection of outliers vs. regular objects, which is not adequate for many applications. Traditional outlier ranking(More)
Clustering methods based on modularity are well-established and widely used for graph data. However, today's applications store additional attribute information for each node in the graph. This attribute information may even be contradicting with the graph structure, which raises a major challenge for the simultaneous mining of both information sources. For(More)
Clustering high dimensional data is an emerging research field. Subspace clustering or projected clustering group similar objects in subspaces, i.e. projections, of the full space. In the past decade, several clustering paradigms have been developed in parallel, without thorough evaluation and comparison between these paradigms on a common basis. Conclusive(More)
To gain insight into today's large data resources, data mining provides automatic aggregation techniques. Clustering aims at grouping data such that objects within groups are similar while objects in different groups are dissimilar. In scenarios with many attributes or with noise, clusters are often hidden in subspaces of the data and do not show up in the(More)
—Outlier mining is an important data analysis task to distinguish exceptional outliers from regular objects. For outlier mining in the full data space, there are well established methods which are successful in measuring the degree of deviation for out-lier ranking. However, in recent applications traditional outlier mining approaches miss outliers as they(More)
Subspace clustering aims at detecting clusters in any subspace projection of a high dimensional space. As the number of projections is exponential in the number of dimensions , efficiency is crucial. Moreover, the resulting sub-space clusters are often highly redundant, i.e. many clusters are detected multiply in several projections. We propose a novel(More)
To gain insight into today's large data resources, data mining extracts interesting patterns. To generate knowledge from patterns and benefit from human cognitive abilities, meaningful visualization of patterns are crucial. Clustering is a data mining technique that aims at grouping data to patterns based on mutual (dis)similarity. For high dimensional(More)
cluster definition " Group similar objects in one group, separating dissimilar objects in different groups. " Several instances focus on: different similarity functions, cluster characteristics, data types,. .. Most definitions provide only a single clustering solution For example, K-MEANS Aims at a single partitioning of the data Each object is assigned to(More)