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Chapter 8: Introduction to Pattern Discovery

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3 Chapter 8: Introduction to Pattern Discovery 8.1 Introduction 8.2 Cluster Analysis 8.3 Market Basket Analysis (Self-Study) – PowerPoint PPT presentation

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Title: Chapter 8: Introduction to Pattern Discovery


1
3
Chapter 8 Introduction to Pattern Discovery
8.1 Introduction
8.2 Cluster Analysis
8.3 Market Basket Analysis (Self-Study)
2
3
Chapter 8 Introduction to Pattern Discovery
8.1 Introduction
8.2 Cluster Analysis
8.3 Market Basket Analysis (Self-Study)
3
Pattern Discovery
The Essence of Data Mining? the discovery of
interesting, unexpected, or valuable structures
in large data sets. David Hand
...
3
4
Pattern Discovery
The Essence of Data Mining? the discovery of
interesting, unexpected, or valuable structures
in large data sets. David Hand
If youve got terabytes of data, and youre
relying on data mining to find interesting things
in there for you, youve lost before youve even
begun.
Herb Edelstein
4
5
Pattern Discovery Caution
  • Poor data quality
  • Opportunity
  • Interventions
  • Separability
  • Obviousness
  • Non-stationarity

5
6
Pattern Discovery Applications
Data reduction Novelty detection Profiling Market
basket analysis Sequence analysis
C
A
B
...
6
7
Pattern Discovery Tools
Data reduction Novelty detection Profiling Market
basket analysis Sequence analysis
C
A
B
...
7
8
Pattern Discovery Tools
Data reduction Novelty detection Profiling Market
basket analysis Sequence analysis
C
A
B
8
9
3
Chapter 8 Introduction to Pattern Discovery
8.1 Introduction
8.2 Cluster Analysis
8.3 Market Basket Analysis (Self-Study)
10
Unsupervised Classification
cluster 1
Unsupervised classification grouping of cases
based on similarities in input values.
cluster 2
cluster 3
cluster 1
cluster 2
...
10
11
Unsupervised Classification
cluster 1
Unsupervised classification grouping of cases
based on similarities in input values.
cluster 2
cluster 3
cluster 1
cluster 2
...
11
12
k-means Clustering Algorithm
Training Data
1. Select inputs. 2. Select k cluster
centers. 3. Assign cases to closest
center. 4. Update cluster centers. 5. Re-assign
cases. 6. Repeat steps 4 and 5 until
convergence.
12
13
k-means Clustering Algorithm
Training Data
1. Select inputs. 2. Select k cluster
centers. 3. Assign cases to closest
center. 4. Update cluster centers. 5. Re-assign
cases. 6. Repeat steps 4 and 5 until
convergence.
13
14
k-means Clustering Algorithm
Training Data
1. Select inputs. 2. Select k cluster
centers. 3. Assign cases to closest
center. 4. Update cluster centers. 5. Reassign
cases. 6. Repeat steps 4 and 5 until
convergence.
...
14
15
k-means Clustering Algorithm
Training Data
1. Select inputs. 2. Select k cluster
centers. 3. Assign cases to closest
center. 4. Update cluster centers. 5. Reassign
cases. 6. Repeat steps 4 and 5 until
convergence.
...
15
16
k-means Clustering Algorithm
Training Data
1. Select inputs. 2. Select k cluster
centers. 3. Assign cases to closest
center. 4. Update cluster centers. 5. Reassign
cases. 6. Repeat steps 4 and 5 until
convergence.
...
16
17
k-means Clustering Algorithm
Training Data
1. Select inputs. 2. Select k cluster
centers. 3. Assign cases to closest
center. 4. Update cluster centers. 5. Reassign
cases. 6. Repeat steps 4 and 5 until
convergence.
...
17
18
k-means Clustering Algorithm
Training Data
1. Select inputs. 2. Select k cluster
centers. 3. Assign cases to closest
center. 4. Update cluster centers. 5. Reassign
cases. 6. Repeat steps 4 and 5 until
convergence.
...
18
19
k-means Clustering Algorithm
Training Data
1. Select inputs. 2. Select k cluster
centers. 3. Assign cases to closest
center. 4. Update cluster centers. 5. Reassign
cases. 6. Repeat steps 4 and 5 until
convergence.
...
19
20
k-means Clustering Algorithm
Training Data
1. Select inputs. 2. Select k cluster
centers. 3. Assign cases to closest
center. 4. Update cluster centers. 5. Reassign
cases. 6. Repeat steps 4 and 5 until
convergence.
...
20
21
k-means Clustering Algorithm
Training Data
1. Select inputs. 2. Select k cluster
centers. 3. Assign cases to closest
center. 4. Update cluster centers. 5. Reassign
cases. 6. Repeat steps 4 and 5 until
convergence.
...
21
22
k-means Clustering Algorithm
Training Data
1. Select inputs. 2. Select k cluster
centers. 3. Assign cases to closest
center. 4. Update cluster centers. 5. Reassign
cases. 6. Repeat steps 4 and 5 until
convergence.
...
22
23
k-means Clustering Algorithm
Training Data
1. Select inputs. 2. Select k cluster
centers. 3. Assign cases to closest
center. 4. Update cluster centers. 5. Reassign
cases. 6. Repeat steps 4 and 5 until
convergence.
...
23
24
k-means Clustering Algorithm
Training Data
1. Select inputs. 2. Select k cluster
centers. 3. Assign cases to closest
center. 4. Update cluster centers. 5. Reassign
cases. 6. Repeat steps 4 and 5 until
convergence.
...
24
25
k-means Clustering Algorithm
Training Data
1. Select inputs. 2. Select k cluster
centers. 3. Assign cases to closest
center. 4. Update cluster centers. 5. Reassign
cases. 6. Repeat steps 4 and 5 until
convergence.
...
25
26
k-means Clustering Algorithm
Training Data
1. Select inputs. 2. Select k cluster
centers. 3. Assign cases to closest
center. 4. Update cluster centers. 5. Reassign
cases. 6. Repeat steps 4 and 5 until
convergence.
...
26
27
Segmentation Analysis
Training Data
When no clusters exist, use the k-means algorithm
to partition cases into contiguous groups.
27
28
Demographic Segmentation Demonstration
Analysis goal
Group geographic regions into segments based on
income, household size, and population density.
Analysis plan
  • Select and transform segmentation inputs.
  • Select the number of segments to create.
  • Create segments with the Cluster tool.
  • Interpret the segments.

28
29
Segmenting Census Data
  • This demonstration introduces SAS Enterprise
    Miner tools and techniques for cluster and
    segmentation analysis.

29
30
Exploring and Filtering Analysis Data
  • This demonstration introduces SAS Enterprise
    Miner tools and techniques that explore and
    filteranalysis data, particularly data source
    exploration and case filtering.

30
31
Setting Cluster Tool Options
  • This demonstration illustrates how to use the
    Cluster tool to segment the cases in the
    CENSUS2000 data set.

31
32
Creating Clusters with the Cluster Tool
  • This demonstration illustrates how the Cluster
    tool determines the number of clusters in the
    data.

32
33
Specifying the Segment Count
  • This demonstration illustrates how you can
    change the number of clusters created by the
    Cluster node.

33
34
Exploring Segments
  • This demonstration illustrates how to use
    graphical aids to explore the segments.

35
Profiling Segments
  • This demonstration illustrates using the Segment
    Profile tool to interpret the composition of
    clusters.

36
Exercises
  • This exercise reinforces the concepts discussed
    previously.

36
37
3
Chapter 8 Introduction to Pattern Discovery
8.1 Introduction
8.2 Cluster Analysis
8.3 Market Basket Analysis (Self-Study)
38
Market Basket Analysis
...
38
39
Market Basket Analysis
...
39
40
Implication?
Checking Account
No
Yes
4,000
No
Savings Account
6,000
Yes
10,000
40
41
Barbie Doll ? Candy
  • Put them closer together in the store.
  • Put them far apart in the store.
  • Package candy bars with the dolls.
  • Package Barbie candy poorly selling item.
  • Raise the price on one, and lower it on the
    other.
  • Offer Barbie accessories for proofs of purchase.
  • Do not advertise candy and Barbie together.
  • Offer candies in the shape of a Barbie doll.

41
42
Data Capacity
42
43
Association Tool Demonstration
Analysis goal
Explore associations between retail banking
services used by customers.
Analysis plan
  • Create an association data source.
  • Run an association analysis.
  • Interpret the association rules.
  • Run a sequence analysis.
  • Interpret the sequence rules.

43
44
Market Basket Analysis
  • This demonstration illustrates how to conduct
    market basket analysis.

45
Sequence Analysis
  • This demonstration illustrates how to conduct a
    sequence analysis.

46
Pattern Discovery Tools Review
Generate cluster models using automatic settings
and segmentation models with user-defined
settings.
Compare within-segment distributions ofselected
inputs to overall distributions. Thishelps you
understand segment definition.
Conduct market basket and sequence analysis on
transactions data. A data source must have one
target, one ID, and (if desired) one sequence
variable in the data source.
46
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