https://ssk015.github.io/dashuju3/
Click here Slide(pdf)
What is Clustering?
That is to say, given a set of points, We can define a concept of distance between these points. Then, we group the points into some number of clusters, which is known as “簇” in Chinese.
distance : mainly Euclidean or Jaccard
Why is Clustering hard?
Too many dimensions: isolated points
Two main methods:
Hierarchical: bottom up and top down
Assign: assign points to a Existing cluster
Hierarchical(between clusters):
note:
represent a cluster.
determine the nearness of clusters.
when to stop merging clusters.
Euclidean case:
centroid(average)
(1) distance of centroids
(2) shortest distance between two clusters
UnEuclidean case:
Approach 1
choose Clustroid(a exisiting point)
maxium/average/square of dis
various distance and cohesion measures
Approach 2
the collection of points.
define inter-cluster distance.
min of two or avg of all pairs.
Approach 3
the collection of points.
define a notion of cohesion, merge similiar unions.
diamter, avg dis, density
3.
design 1: convex clusters
design 2: concentric clusters.
K-Means(Assignment)
definition: a method, not a algorithm.
method: Before convergence(all the points don’t move), assign points and update centroids
select k: try different k, get the value when avg dis to centroid stop changing dramatically.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent