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1 Pattern Discovery: an Example with Sequential Patterns [Agrawal and Srikant 1996] Support “How many people?” 6 Frequency “What proportion?” 6/10 A frequent behavior is followed by at least 50% of people is a frequent sequence! Rosa Frédéric k François Bruno Pascal Osmar Sandra Maguelon ne Julien Alberto “Rosa had lunch, then later put on some sunglasses while going to the beach” <( )( )> <( )( )> <( )( )( )> <( )( )( )> <( )( )> <( )( )> <( ) ( )> <( )( )( )> <( )( )( )> <( ) ( )> <( )( )>

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Pattern Discovery: an Example with Sequential Patterns [Agrawal and Srikant 1996]. . . “ Rosa had lunch , then later put on some sunglasses while going to the beach ”. . . - PowerPoint PPT Presentation

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Page 1: Pattern Discovery: an Example with Sequential Patterns [Agrawal and Srikant 1996]

1Pattern Discovery: an Example with Sequential Patterns [Agrawal and Srikant 1996]

Support “How many people?” 6Frequency “What proportion?” 6/10

A frequent behavior is followed by at least 50% of people

is a frequent sequence!

Rosa

Frédérick

François

Bruno

Pascal

Osmar

Sandra

Maguelonne

Julien

Alberto

“Rosa had lunch, then later put on some sunglasses while going to

the beach”

<( )( )>

<( )( )>

<( )( )( )>

<( )( )( )>

<( )( )>

<( )( )>

<( )( )>

<( )( )( )>

<( )( )( )>

<( )( )>

<( )( )>

Page 2: Pattern Discovery: an Example with Sequential Patterns [Agrawal and Srikant 1996]

2Pattern Discovery: What if contextual information is available?

Rosa

Frédérick

François

Bruno

Pascal

Osmar

Sandra

Maguelonne

Julien

Alberto

Contextual Information

Working or Not working

<( )( )( )>

<( )( )( )>

<( )( )>

<( )( )>

<( )( )>

<( )( )( )>

<( )( )( )>

<( )( )>

<( )( )>

Page 3: Pattern Discovery: an Example with Sequential Patterns [Agrawal and Srikant 1996]

3

Rosa

Frédérick

François

Bruno

Pascal

Osmar

Sandra

Maguelonne

Julien

Alberto

Pattern Discovery: What if contextual information is available?

Contextual Information

Working or Not working

Sunny or Rainy

is frequent but:

• 6 out of 6 working

• none not working

Problem: larger proportion of working people

is frequent but:

• 6 out of 6 working

• none not working

Problem: larger proportion of working people

<( )( )>

<( )( )( )>

<( )( )( )>

<( )( )>

<( )( )>

<( )( )>

<( )( )( )>

<( )( )( )>

<( )( )>

<( )( )>

Page 4: Pattern Discovery: an Example with Sequential Patterns [Agrawal and Srikant 1996]

4Pattern Discovery: What if contextual information is available?

Rosa

Frédérick

François

Bruno

Pascal

Osmar

Sandra

Maguelonne

Julien

Alberto

Contextual Information

Working or Not working

Sunny or Rainy

is frequent but:

• 6 out of 6 working

• none not working

Problem: larger proportion of working people

is frequent but:

• 6 out of 6 working

• none not working

Problem: larger proportion of working people

<( )( )>

<( )( )( )>

<( )( )( )>

<( )( )>

<( )( )>

<( )( )>

<( )( )( )>

<( )( )( )>

<( )( )>

<( )( )>

Page 5: Pattern Discovery: an Example with Sequential Patterns [Agrawal and Srikant 1996]

5Pattern Discovery: What if contextual information is available?

Rosa

Frédérick

François

Bruno

Pascal

Osmar

Sandra

Maguelonne

Julien

Alberto

Contextual Information

Working or Not working

Sunny or Rainy

is NOT frequent but:

• All not working and sunny

• Does NOT appear elsewhere

Problem: too small proportion of not working and sunny

is NOT frequent but:

• All not working and sunny

• Does NOT appear elsewhere

Problem: too small proportion of not working and sunny

<( )>

<( )( )( )>

<( )( )( )>

<( )( )>

<( )( )>

<( )( )>

<( )( )( )>

<( )( )( )>

<( )( )>

<( )( )>

Page 6: Pattern Discovery: an Example with Sequential Patterns [Agrawal and Srikant 1996]

6Pattern Discovery: What if contextual information is available?

Rosa

Frédérick

François

Bruno

Pascal

Osmar

Sandra

Maguelonne

Julien

Alberto

Contextual Information

Working or Not working

Sunny or Rainy

is NOT frequent but:

• All not working and sunny

• Does NOT appear elsewhere

Problem: too small proportion of not working and sunny

is NOT frequent but:

• All not working and sunny

• Does NOT appear elsewhere

Problem: too small proportion of not working and sunny

<( )>

<( )( )( )>

<( )( )( )>

<( )( )>

<( )( )>

<( )( )>

<( )( )( )>

<( )( )( )>

<( )( )>

<( )( )>

Page 7: Pattern Discovery: an Example with Sequential Patterns [Agrawal and Srikant 1996]

7Pattern Discovery: What if contextual information is available?

Rosa

Frédérick

François

Bruno

Pascal

Osmar

Sandra

Maguelonne

Julien

Alberto

Contextual Information

Working or Not working

Sunny or Rainy

is frequent:

• For all day types

• Considered as generally frequent

is frequent:

• For all day types

• Considered as generally frequent

<( )>

<( )( )( )>

<( )( )( )>

<( )( )>

<( )( )>

<( )( )>

<( )( )( )>

<( )( )( )>

<( )( )>

<( )( )>

Page 8: Pattern Discovery: an Example with Sequential Patterns [Agrawal and Srikant 1996]

8Pattern Discovery: What if contextual information is available?

Rosa

Frédérick

François

Bruno

Pascal

Osmar

Sandra

Maguelonne

Julien

Alberto

Contextual Information

Working or Not working

Sunny or Rainy

is frequent:

• For all day types

• Considered as generally frequent

is frequent:

• For all day types

• Considered as generally frequent

<( )>

<( )( )( )>

<( )( )( )>

<( )( )>

<( )( )>

<( )( )>

<( )( )( )>

<( )( )( )>

<( )( )>

<( )( )>


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1 Pattern Discovery: an Example with Sequential Patterns [Agrawal and Srikant 1996] Support “How many people?” 6 Frequency “What proportion?” 6/10 A frequent