Hierarchical Clustering for Finding Symmetries and Other Patterns in Massive, High Dimensional Datasets

Details Hierarchical Clustering for Finding Symmetries and Other Patterns in Massive, High Dimensional Datasets Hierarchical Clustering for Finding Symmetries and Other Patterns in Massive, High Dimensional Datasets

2010-05-14

arxiv.org/abs/1005.2638v1

Statistics

Machine Learning

41 pages, 13 figures, 6 tables. 81 references

Scientific paper

Data analysis and data mining are concerned with unsupervised pattern finding and structure determination in data sets. "Structure" can be understood as symmetry and a range of symmetries are expressed by hierarchy. Such symmetries directly point to invariants, that pinpoint intrinsic properties of the data and of the background empirical domain of interest. We review many aspects of hierarchy here, including ultrametric topology, generalized ultrametric, linkages with lattices and other discrete algebraic structures and with p-adic number representations. By focusing on symmetries in data we have a powerful means of structuring and analyzing massive, high dimensional data stores. We illustrate the powerfulness of hierarchical clustering in case studies in chemistry and finance, and we provide pointers to other published case studies.

Affiliated with

Also associated with

No associations

Rating

Hierarchical Clustering for Finding Symmetries and Other Patterns in Massive, High Dimensional Datasets does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Hierarchical Clustering for Finding Symmetries and Other Patterns in Massive, High Dimensional Datasets, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Hierarchical Clustering for Finding Symmetries and Other Patterns in Massive, High Dimensional Datasets will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-240789