Cluster analysis and SUBCLU

Shortcuts: Differences, Similarities, Jaccard Similarity Coefficient, References.

Difference between Cluster analysis and SUBCLU

Cluster analysis vs. SUBCLU

Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense) to each other than to those in other groups (clusters). SUBCLU is an algorithm for clustering high-dimensional data by Karin Kailing, Hans-Peter Kriegel and Peer Kröger.

Similarities between Cluster analysis and SUBCLU

Cluster analysis and SUBCLU have 3 things in common (in Unionpedia): Clustering high-dimensional data, DBSCAN, Hans-Peter Kriegel.

Clustering high-dimensional data

Clustering high-dimensional data is the cluster analysis of data with anywhere from a few dozen to many thousands of dimensions.

Cluster analysis and Clustering high-dimensional data · Clustering high-dimensional data and SUBCLU · See more »

DBSCAN

Density-based spatial clustering of applications with noise (DBSCAN) is a data clustering algorithm proposed by Martin Ester, Hans-Peter Kriegel, Jörg Sander and Xiaowei Xu in 1996.

Cluster analysis and DBSCAN · DBSCAN and SUBCLU · See more »

Hans-Peter Kriegel

Hans-Peter Kriegel (1 October 1948, Germany) is a German computer scientist and professor at the Ludwig Maximilian University of Munich and leading the Database Systems Group in the Department of Computer Science.

Cluster analysis and Hans-Peter Kriegel · Hans-Peter Kriegel and SUBCLU · See more »

The list above answers the following questions

What Cluster analysis and SUBCLU have in common
What are the similarities between Cluster analysis and SUBCLU

Cluster analysis and SUBCLU Comparison

Cluster analysis has 169 relations, while SUBCLU has 10. As they have in common 3, the Jaccard index is 1.68% = 3 / (169 + 10).

References

This article shows the relationship between Cluster analysis and SUBCLU. To access each article from which the information was extracted, please visit:

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