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Hierarchical Clustering

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Hierarchical Clustering Dr. Bernard Chen Assistant Professor Outline Hierarchical Clustering Hybrid Hierarchical Kmeans clustering DBscan Hierarchical Clustering ... – PowerPoint PPT presentation

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Title: Hierarchical Clustering


1
Hierarchical Clustering
  • Dr. Bernard Chen
  • Assistant Professor

2
Outline
  • Hierarchical Clustering
  • Hybrid Hierarchical Kmeans clustering
  • DBscan

3
Hierarchical Clustering
Venn Diagram of Clustered Data
Dendrogram
From http//www.stat.unc.edu/postscript/papers/mar
ron/Stat321FDA/RimaIzempresentation.ppt
4
Nearest Neighbor, Level 2, k 1 clusters.
From http//www.stat.unc.edu/postscript/papers/mar
ron/Stat321FDA/RimaIzempresentation.ppt
5
Nearest Neighbor, Level 3, k 2 clusters.
6
Nearest Neighbor, Level 4, k 3 clusters.
7
Nearest Neighbor, Level 5, k 2 clusters.
8
Nearest Neighbor, Level 6, k 2 clusters.
9
Nearest Neighbor, Level 7, k 2 clusters.
10
Nearest Neighbor, Level 8, k 1 cluster.
11
Typical Alternatives to Calculate the Distance
between Clusters
  • Single link smallest distance between an
    element in one cluster and an element in the
    other, i.e., dis(Ki, Kj) min(tip, tjq)
  • Complete link largest distance between an
    element in one cluster and an element in the
    other, i.e., dis(Ki, Kj) max(tip, tjq)
  • Average avg distance between an element in one
    cluster and an element in the other, i.e.,
    dis(Ki, Kj) avg(tip, tjq)

12
Functional significant gene clusters
Two-way clustering
Sample clusters
Gene clusters
13
Outline
  • Hierarchical Clustering
  • Hybrid Hierarchical Kmeans clustering
  • DBscan

14
Motivation
  • Among clustering algorithms, Hierarchical and
    K-means clustering are the two most popular and
    classic methods. However, both have their innate
    disadvantages.
  • K-means clustering requires a specified number
    of clusters in advance and chooses initial
    centroids randomly in other words, you dont
    know how to start
  • Hierarchical clustering is hard to find a place
    to cut

15
Hybrid Hierarchical K-means Clustering (HHK)
Algorithm
  • The brief idea is we cluster around half data
    through Hierarchical clustering and succeed by
    K-means for the remaining
  • In order to generate super-rules, we let
    Hierarchical terminate when it generates the
    largest number of clusters

16
Hybrid Hierarchical K-means Clustering (HHK)
Algorithm
17
Hybrid Hierarchical K-means Clustering (HHK)
Algorithm Example

18
Hybrid Hierarchical K-means Clustering (HHK)
Algorithm Example

19
Hybrid Hierarchical K-means Clustering (HHK)
Algorithm Example

20
Hybrid Hierarchical K-means Clustering (HHK)
Algorithm Example

21
Hybrid Hierarchical K-means Clustering (HHK)
Algorithm Example

22
Hybrid Hierarchical K-means Clustering (HHK)
Algorithm Example

23
Hybrid Hierarchical K-means Clustering (HHK)
Algorithm Example
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