%0 Conference Paper %1 Schikuta97thebang-clustering %A Schikuta, Erich %A Erhart, Martin %B Proc. Sec. Int. Symp. IDA-97 %D 1997 %I Springer-Verlag %K bang clustering data-mining grid %P 513--524 %T The BANG-Clustering System: Grid-Based Data Analysis %U http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.55.8889 %X . For the analysis of large images the clustering of the data set is a common technique to identify correlation characteristics of the underlying value space. In this paper a new approach to hierarchical clustering of very large data sets is presented. The BANG-Clustering system presented in this paper is a novel approach to hierarchical data analysis. It is based on the BANG-Clustering method (Sch96) and uses a multidimensional grid data structure to organize the value space surrounding the pattern values. The patterns are grouped into blocks and clustered with respect to the blocks by a topological neighbor search algorithm. 1 Introduction Clustering methods are extremely important for explorative data analysis, which is an important approach for the analysis of images. Previously presented algorithms can be divided into hierarchical algorithms, e.g. single-linkage, completelinkage, etc. and partitional algorithms, e.g. K-MEANS, ISODATA, etc. (see DJ80). All of these methods suf...

@inproceedings{Schikuta97thebang-clustering, abstract = {. For the analysis of large images the clustering of the data set is a common technique to identify correlation characteristics of the underlying value space. In this paper a new approach to hierarchical clustering of very large data sets is presented. The BANG-Clustering system presented in this paper is a novel approach to hierarchical data analysis. It is based on the BANG-Clustering method ([Sch96]) and uses a multidimensional grid data structure to organize the value space surrounding the pattern values. The patterns are grouped into blocks and clustered with respect to the blocks by a topological neighbor search algorithm. 1 Introduction Clustering methods are extremely important for explorative data analysis, which is an important approach for the analysis of images. Previously presented algorithms can be divided into hierarchical algorithms, e.g. single-linkage, completelinkage, etc. and partitional algorithms, e.g. K-MEANS, ISODATA, etc. (see [DJ80]). All of these methods suf...}, added-at = {2009-09-09T17:20:05.000+0200}, author = {Schikuta, Erich and Erhart, Martin}, biburl = {https://www.bibsonomy.org/bibtex/24cb482e9cf11ca9a641eb5f98fae21d3/beate}, booktitle = {Proc. Sec. Int. Symp. IDA-97}, description = {CiteSeerX — The BANG-Clustering System: Grid-Based Data Analysis}, interhash = {295146a53ec69c2c0df5187e023b0ddf}, intrahash = {4cb482e9cf11ca9a641eb5f98fae21d3}, keywords = {bang clustering data-mining grid}, pages = {513--524}, publisher = {Springer-Verlag}, timestamp = {2009-09-09T17:20:05.000+0200}, title = {The BANG-Clustering System: Grid-Based Data Analysis}, url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.55.8889}, year = 1997 }