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Fixed titles, math rendering, and links on some pages

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Brandon Rozek 2021-07-26 09:13:20 -04:00
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content/research/clusteranalysis/notes/lec12.md
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@ -1,4 +1,8 @@
# Introduction to Density Based Clustering
---
title: Introduction to Density Based Clustering
showthedate: false
math: true
---
In density-based clustering, clusters are defined as areas of higher density than the remainder of the data sets. Objects in more sparse areas are considered to be outliers or border points. This helps discover clusters of arbitrary shape.
@ -31,7 +35,7 @@ A cluster then satisfies two properties:
2. Find the connected components of *core* points on the neighborhood graph, ignoring all non-core points.
3. Assign each non-core point to a nearby cluster if the cluster is an $\epsilon$ (eps) neighbor, otherwise assign it to noise.
###Advantages
### Advantages
- Does not require one to specify the number of clusters in the data
- Can find arbitrarily shaped clusters
@ -53,4 +57,4 @@ $\epsilon$: Ideally the $k^{th}$ nearest neighbors are at roughly the same dista
Example of Run Through
https://www.cse.buffalo.edu/~jing/cse601/fa12/materials/clustering_density.pdf
https://www.cse.buffalo.edu/~jing/cse601/fa12/materials/clustering_density.pdf