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- Emmanuel Müller, Stephan Günnemann, Ira Assent, Thomas Seidl
- PVLDB
- 2009

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)

- Fabian Keller, Emmanuel Müller, Klemens Böhm
- 2012 IEEE 28th International Conference on Data…
- 2012

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 listings due to low contrast between outliers and regular… (More)

- Ira Assent, Ralph Krieger, Emmanuel Müller, Thomas Seidl
- Seventh IEEE International Conference on Data…
- 2007

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)

- Ira Assent, Ralph Krieger, Emmanuel Müller, Thomas Seidl
- 2008 Eighth IEEE International Conference on Data…
- 2008

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 subspace clusters are often highly redundant, i.e. many clusters are detected multiply in several projections. We propose a novel index… (More)

- Emmanuel Müller, Matthias Schiffer, Thomas Seidl
- 2011 IEEE 27th International Conference on Data…
- 2011

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 outlier ranking. However, in recent applications traditional outlier mining approaches miss outliers as they… (More)

Graph clustering and graph outlier detection have been studied extensively on plain graphs, with various applications. Recently, algorithms have been extended to graphs with attributes as often observed in the real-world. However, all of these techniques fail to incorporate the user preference into graph mining, and thus, lack the ability to steer… (More)

- Ira Assent, Ralph Krieger, Emmanuel Müller, Thomas Seidl
- SIGKDD Explorations
- 2007

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)

- Emmanuel Müller, Ira Assent, Stephan Günnemann, Ralph Krieger, Thomas Seidl
- 2009 Ninth IEEE International Conference on Data…
- 2009

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)

- Emmanuel Müller, Stephan Günnemann, Ines Färber, Thomas Seidl
- 2012 IEEE 28th International Conference on Data…
- 2010

Traditional clustering algorithms identify just a single clustering of the data. Today's complex data, however, allow multiple interpretations leading to several valid groupings hidden in different views of the database. Each of these multiple clustering solutions is valuable and interesting as different perspectives on the same data and several meaningful… (More)

- Emmanuel Müller, Ira Assent, Patricia Iglesias Sánchez, Yvonne Mülle, Klemens Böhm
- 2012 IEEE 12th International Conference on Data…
- 2012

Outlier mining is an important task for finding anomalous objects. In practice, however, there is not always a clear distinction between outliers and regular objects as objects have different roles w.r.t. different attribute sets. An object may deviate in one subspace, i.e. a subset of attributes. And the same object might appear perfectly regular in other… (More)