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My8clst
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Clustering Feature Vector
Clustering Feature: CF = (N, LS, SS) N : Number of data points LS: N i=1 X i SS: N i=1 (X i ) 2 CF = (5, (16,30),244) (3,4) (2,6) (4,5) (4,7) (3,8)
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Some Characteristics of
CF
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CF Tree CF
1 child 1 CF 3 child 3 CF 2 child 2 CF 5 child 5 CF 1 CF 2 CF 6 prev next CF 1 CF 2 CF 4 prev next B = 7 L = 6 Root Non-leaf node Leaf node Leaf node CF 1 child 1 CF 3 child 3 CF 2 child 2 CF 6 child 6
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Motivation: K-means is
not good
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Example of CHAMELEON
Construct Sparse Graph Partition the Graph Merge Partition Final Clusters Data Set
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Reachability-distance Cluster-order of
the objects undefined ‘
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Density-Based Cluster analysis:
OPTICS & Its Applications
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Density Attractor
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Center-Defined and Arbitrary
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What is Wavelet
(1)?
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What Is Wavelet
(2)?
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Quantization
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Transformation
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Salary (10,000) 20
30 40 50 60 age 5 4 3 1 2 6 7 0 = 3 20 30 40 50 60 age 5 4 3 1 2 6 7 0 Vacation(week) age Vacation Salary 30 50
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COBWEB Clustering Method
A classification tree
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Model-Based Clustering Methods
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Clustering With Obstacle
Objects Taking obstacles into account Not Taking obstacles into account
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![Cluster Analysis ,[object Object],[object Object],[object Object],modified by Donghui Zhang](https://image.slidesharecdn.com/my8clst-1206370412222810-5/85/My8clst-1-320.jpg)
![Chapter 7. Cluster Analysis ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)