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1
Intro
2
Data Analysis
3
Classification
4
Clustering
5
Higher Dimensional Geometry
6
Geometry and Topology
7
Geometry and Ecology
8
Similarity Graph
9
Ontology
10
The bottleneck
11
Analysis of one way
12
Eigenvectors
13
Exponential Partitions
14
Partitioning Graphs
15
Knots
16
Scanning
17
Practical Application
18
Complex Application
Description:
Explore the intersection of hyperbolic geometry and data clustering in this 39-minute conference talk by Jesse Johnson at BDF 2015. Discover how concepts from knot theory and hyperbolic three-manifolds led to the development of the Topologically Intrinsic Lexicographic Ordering (TILO) clustering algorithm. Delve into topics such as data analysis, classification, higher dimensional geometry, topology, similarity graphs, ontology, eigenvectors, exponential partitions, and knots. Learn how geometric patterns and shapes in data collections can be leveraged to solve common data science problems using abstract geometric tools. Gain insights into practical and complex applications of these concepts in fields like ecology and beyond.

From Hyperbolic Geometry to Data Clustering

Open Data Science
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