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Chap8 basic cluster_analysis
1.
Data Mining Cluster
Analysis: Basic Concepts and Algorithms Lecture Notes for Chapter 8 Introduction to Data Mining by Tan, Steinbach, Kumar © Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004
2.
3.
4.
5.
Notion of a
Cluster can be Ambiguous How many clusters? Four Clusters Two Clusters Six Clusters
6.
7.
Partitional Clustering Original
Points A Partitional Clustering
8.
Hierarchical Clustering Traditional
Hierarchical Clustering Non-traditional Hierarchical Clustering Non-traditional Dendrogram Traditional Dendrogram
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
Two different K-means
Clusterings Original Points Sub-optimal Clustering Optimal Clustering
23.
Importance of Choosing
Initial Centroids
24.
Importance of Choosing
Initial Centroids
25.
26.
Importance of Choosing
Initial Centroids …
27.
Importance of Choosing
Initial Centroids …
28.
29.
10 Clusters Example
Starting with two initial centroids in one cluster of each pair of clusters
30.
10 Clusters Example
Starting with two initial centroids in one cluster of each pair of clusters
31.
10 Clusters Example
Starting with some pairs of clusters having three initial centroids, while other have only one.
32.
10 Clusters Example
Starting with some pairs of clusters having three initial centroids, while other have only one.
33.
34.
35.
36.
37.
38.
Bisecting K-means Example
39.
40.
Limitations of K-means:
Differing Sizes Original Points K-means (3 Clusters)
41.
Limitations of K-means:
Differing Density Original Points K-means (3 Clusters)
42.
Limitations of K-means:
Non-globular Shapes Original Points K-means (2 Clusters)
43.
44.
Overcoming K-means Limitations
Original Points K-means Clusters
45.
Overcoming K-means Limitations
Original Points K-means Clusters
46.
47.
48.
49.
50.
51.
52.
53.
54.
55.
56.
57.
58.
59.
60.
Hierarchical Clustering: MIN
Nested Clusters Dendrogram 1 2 3 4 5 6 1 2 3 4 5
61.
62.
63.
64.
Hierarchical Clustering: MAX
Nested Clusters Dendrogram 1 2 3 4 5 6 1 2 5 3 4
65.
66.
67.
68.
Hierarchical Clustering: Group
Average Nested Clusters Dendrogram 1 2 3 4 5 6 1 2 5 3 4
69.
70.
71.
Hierarchical Clustering: Comparison
Group Average Ward’s Method MIN MAX 1 2 3 4 5 6 1 2 5 3 4 1 2 3 4 5 6 1 2 5 3 4 1 2 3 4 5 6 1 2 5 3 4 1 2 3 4 5 6 1 2 3 4 5
72.
73.
74.
75.
76.
77.
DBSCAN: Core, Border,
and Noise Points
78.
79.
DBSCAN: Core, Border
and Noise Points Original Points Point types: core , border and noise Eps = 10, MinPts = 4
80.
81.
82.
83.
84.
Clusters found in
Random Data Random Points K-means DBSCAN Complete Link
85.
86.
87.
88.
89.
90.
91.
92.
93.
Using Similarity Matrix
for Cluster Validation DBSCAN
94.
95.
96.
97.
98.
99.
100.
101.
102.
103.
External Measures of
Cluster Validity: Entropy and Purity
104.
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