C. Böhm

Publications
Influence

Searching in high-dimensional spaces: Index structures for improving the performance of multimedia databases

C. Böhm, Stefan Berchtold, D. Keim
Computer Science
CSUR
1 September 2001

In this survey we provide an overview of the current state of the art in querying multimedia databases, describing the index structures and algorithms for an efficient query processing in high-dimensional spaces. Expand

Density connected clustering with local subspace preferences

C. Böhm, K. Murthy, H. Kriegel, P. Kröger
Mathematics, Computer Science
Fourth IEEE International Conference on Data…
1 November 2004

In this paper, we introduce the concept of local subspace preferences, which captures the main directions of high point density. Expand

A cost model for nearest neighbor search in high-dimensional data space

Stefan Berchtold, C. Böhm, D. Keim, H. Kriegel
Mathematics, Computer Science
PODS '97
1 May 1997

We present a new cost model for nearest neighbor search in high-dimensional data space which takes boundary effects into account and is applicable to different data distributions and index structures. Expand

Automated detection of brain atrophy patterns based on MRI for the prediction of Alzheimer's disease

C. Plant, S. Teipel, +6 authors M. Ewers
Psychology, Computer Science
NeuroImage
1 March 2010

We developed a novel data mining framework in combination with three different classifiers including support vector machine (SVM), Bayes statistics, and voting feature intervals (VFI) to derive a quantitative index of pattern matching for the prediction of the conversion from MCI to AD. Expand

Independent quantization: an index compression technique for high-dimensional data spaces

Stefan Berchtold, C. Böhm, H. V. Jagadish, H. Kriegel, J. Sander
Computer Science
Proceedings of 16th International Conference on…
29 February 2000

In this paper, we present the IQ-tree, a new index compression technique for high-dimensional data spaces that combines the advantages of hierarchical search and a fast linear search. Expand

The k-Nearest Neighbour Join: Turbo Charging the KDD Process

C. Böhm, F. Krebs
Mathematics, Computer Science
Knowledge and Information Systems
1 November 2004

We propose a new algorithm to compute the k-nearest neighbour join using the multipage index (MuX), a specialised index structure for the similarity join. Expand

Epsilon grid order: an algorithm for the similarity join on massive high-dimensional data

C. Böhm, Bernhard Braunmüller, F. Krebs, H. Kriegel
Computer Science
SIGMOD '01
1 May 2001

We propose the Epsilon Grid Order, a new algorithm for determining the similarity join of very large data sets, which is based on a particular sort order of the data points. Expand

Computing Clusters of Correlation Connected objects

C. Böhm, K. Murthy, P. Kröger, A. Zimek
Computer Science
SIGMOD '04
13 June 2004

In this paper, we propose a method called 4C (Computing Correlation Connected Clusters) to identify local subgroups of the data objects sharing a uniform but arbitrarily complex correlation. Expand

Improving the Query Performance of High-Dimensional Index Structures by Bulk-Load Operations

Stefan Berchtold, C. Böhm, H. Kriegel
Computer Science
EDBT
23 March 1998

In this paper, we propose a new bulk-loading technique for high-dimensional indexes which represent an important component of multimedia database systems. Expand

Density-based clustering using graphics processors

C. Böhm, R. Noll, C. Plant, Bianca Wackersreuther
Computer Science
CIKM
2 November 2009

In this paper, we propose CUDA-DClust, a massively parallel algorithm for density-based clustering for the use of a Graphics Processing Unit (GPU). Expand

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