2. BigML, Inc 2
Poul Petersen
CIO, BigML, Inc.
Association DiscoveryFinding Interesting Correlations
3. BigML, Inc 3Association Discovery
Association Discovery
• Algorithm: “Magnum Opus” from Geoff Webb
• Unsupervised Learning: Works with unlabelled
data, like clustering and anomaly detection.
• Learning Task: Find “interesting” relations
between variables.
4. BigML, Inc 4Association Discovery
Unsupervised Learning
date customer account auth class zip amount
Mon Bob 3421 pin clothes 46140 135
Tue Bob 3421 sign food 46140 401
Tue Alice 2456 pin food 12222 234
Wed Sally 6788 pin gas 26339 94
Wed Bob 3421 pin tech 21350 2459
Wed Bob 3421 pin gas 46140 83
The Sally 6788 sign food 26339 51
date customer account auth class zip amount
Mon Bob 3421 pin clothes 46140 135
Tue Bob 3421 sign food 46140 401
Tue Alice 2456 pin food 12222 234
Wed Sally 6788 pin gas 26339 94
Wed Bob 3421 pin tech 21350 2459
Wed Bob 3421 pin gas 46140 83
The Sally 6788 sign food 26339 51
Clustering
Anomaly Detection
similar
unusual
5. BigML, Inc 5Association Discovery
Association Rules
date customer account auth class zip amount
Mon Bob 3421 pin clothes 46140 135
Tue Bob 3421 sign food 46140 401
Tue Alice 2456 pin food 12222 234
Wed Sally 6788 pin gas 26339 94
Wed Bob 3421 pin tech 21350 2459
Wed Bob 3421 pin gas 46140 83
The Sally 6788 sign food 26339 51
zip = 46140
amount < 100
Rules:
Antecedent Consequent
{customer = Bob, account = 3421}
{class = gas}
6. BigML, Inc 6Association Discovery
Use Cases
• Market Basket Analysis
• Web usage patterns
• Intrusion detection
• Fraud detection
• Bioinformatics
• Medical risk factors
7. BigML, Inc 7Association Discovery
Magnum Opus
• Select measure of interest: Levarage, Lift, etc
• System finds the top-k associations on that
measure within constraints
• Must be statistically significant interaction between
antecedent and consequent
• Every item in the antecedent must increase the
strength of association
8. BigML, Inc 8Association Discovery
Association Metrics
Instances
A
C
Coverage
Percentage of instances
which match antecedent “A”
9. BigML, Inc 9Association Discovery
Association Metrics
Instances
A
C
Support
Percentage of instances
which match antecedent
“A” and Consequent “C”
10. BigML, Inc 10Association Discovery
Association Metrics
Coverage
Support
Instances
A
C
Confidence
Percentage of instances in
the antecedent which also
contain the consequent.
11. BigML, Inc 11Association Discovery
Association Metrics
C
Instances
A
C
A
Instances
C
Instances
A
Instances
A
C
0% 100%
Instances
A
C
Confidence
A never
implies C
A sometimes
implies C
A always
implies C
12. BigML, Inc 12Association Discovery
Association Metrics
Independent
A
C
C
Observed
A
Lift
Ratio of observed support
to support if A and C were
statistically independent.
Support == Confidence
p(A) * p(C) p(C)
13. BigML, Inc 13Association Discovery
Association Metrics
C
Observed
A
Observed
A
C
< 1 > 1
Independent
A
C
Lift = 1
Negative
Correlation
No Association
Positive
Correlation
Independent
A
C
Independent
A
C
Observed
A
C
14. BigML, Inc 14Association Discovery
Association Metrics
Independent
A
C
C
Observed
A
Leverage
Difference of observed
support and support if A
and C were statistically
independent.
Support - [ p(A) * p(C) ]
15. BigML, Inc 15Association Discovery
Association Metrics
C
Observed
A
Observed
A
C
< 0 > 0
Independent
A
C
Leverage = 0
Negative
Correlation
No Association
Positive
Correlation
Independent
A
C
Independent
A
C
Observed
A
C
-1…
16. BigML, Inc 16Association Discovery
Use Cases
GOAL: Discover “interesting” rules about what store items
are typically purchased together.
• Dataset of 9,834 grocery cart transactions
• Each row is a list of all items in a cart at
checkout
18. BigML, Inc 18Association Discovery
Use Cases
GOAL: Find general rules that indicate diabetes.
• Dataset of diagnostic measurements of 768
patients.
• Each patient labelled True/False for
diabetes.
20. BigML, Inc 20Association Discovery
Medical Risks
Decision Tree
If plasma glucose > 155
and bmi > 29.32
and diabetes pedigree > 0.32
and insulin <= 629
and age <= 44
then diabetes = TRUE
Association Rule
If plasma glucose > 146
then diabetes = TRUE
21. BigML, Inc 21
Poul Petersen
CIO, BigML, Inc.
Topic ModelingDiscovering Meaning in Text
22. BigML, Inc 22Topic Modelling
Unsupervised Learning
Features
Instances
• Learn from instances
• Each instance has features
• There is no label
Clustering
Find similar instances
Anomaly Detection
Find unusual instances
Association Discovery
Find feature rules
23. BigML, Inc 23Topic Modelling
Topic Model
Text Fields
• Unsupervised algorithm
• Learns only from text fields
• Finds hidden topics that model
the text
• How is this different from the Text Analysis
that BigML already offers?
• What does it output and how do we use it
• Unsupervised… model?
Questions:
24. BigML, Inc 24Topic Modelling
Text Analysis
Be not afraid of greatness:
some are born great, some
achieve greatness, and
some have greatness
thrust upon 'em.
great: appears 4 times
Bag of Words
25. BigML, Inc 25Topic Modelling
Text Analysis
… great afraid born achieve … …
… 4 1 1 1 … …
… … … … … … …
Be not afraid of greatness:
some are born great, some achieve
greatness, and some have greatness
thrust upon ‘em.
Model
The token “great”
occurs more than 3 times
The token “afraid”
occurs no more than once
27. BigML, Inc 27Topic Modelling
TA vs TM
Text Analysis Topic Model
Creates thousands of
hidden token counts
Token counts are
independently
uninteresting
No semantic importance
No measure of co-
occurrence
Creates tens of topics
that model the text
Topics are independently
interesting
Semantic meaning
extracted
Support for bigrams
28. BigML, Inc 28Topic Modelling
Generative Modeling
• Decision trees are discriminative models
• Aggressively model the classification boundary
• Parsimonious: Don’t consider anything you don’t have to
• Topic Models are generative models
• Come up with a theory of how the data is generated
• Tweak the theory to fit your data
29. BigML, Inc 29Topic Modelling
Generating Documents
cat shoe zebra
ball tree jump
pen asteroid
cable box step
cabinet yellow
plate flashlight…
shoe asteroid
flashlight
pizza…
plate giraffe
purple jump…
Be not afraid
of greatness:
some are born
great, some
achieve
greatness…
• "Machine" that generates a random word with equal
probability with each pull.
• Pull random number of times to generate a document.
• All documents can be generated, but most are nonsense.
word probability
shoe ϵ
asteroid ϵ
flashlight ϵ
pizza ϵ
… ϵ
30. BigML, Inc 30Topic Modelling
Topic Model
• Written documents have meaning - one way to
describe meaning is to assign a topic.
• For our random machine, the topic can be thought
of as increasing the probability of certain words.
Intuition:
Topic: travel
cat shoe zebra
ball tree jump
pen asteroid
cable box step
cabinet yellow
plate flashlight…
airplane
passport pizza
…
word probability
travel 23,55 %
airplane 2,33 %
mars 0,003 %
mantle ϵ
… ϵ
Topic: space
cat shoe zebra
ball tree jump
pen asteroid
cable box step
cabinet yellow
plate flashlight…
mars quasar
lightyear soda
word probability
space 38,94 %
airplane ϵ
mars 13,43 %
mantle 0,05 %
… ϵ
31. BigML, Inc 31Topic Modelling
Topic Model
plate giraffe
purple
jump…
Topic: "1"
cat shoe zebra
ball tree jump
pen asteroid
cable box step
cabinet yellow
plate flashlight…
word probability
travel 23,55 %
airplane 2,33 %
mars 0,003 %
mantle ϵ
… ϵ
Topic: "k"
cat shoe zebra
ball tree jump
pen asteroid
cable box step
cabinet yellow
plate flashlight…
word probability
shoe 12,12 %
coffee 3,39 %
telephone 13,43 %
paper 4,11 %
… ϵ
…Topic: "2"
cat shoe zebra
ball tree jump
pen asteroid
cable box step
cabinet yellow
plate flashlight…
word probability
space 38,94 %
airplane ϵ
mars 13,43 %
mantle 0,05 %
… ϵ
airplane
passport
pizza …
plate giraffe
purple
jump…
• Each text field in a row is concatenated into a document
• The documents are analyzed to generate "k" related topics
that can model the documents
• Each topic is represented by a distribution of term
probabilities
33. BigML, Inc 33Topic Modelling
Use Cases
• As a preprocessor for other techniques
• Bootstrapping categories for classification
• Recommendation
• Discovery in large, heterogeneous text datasets
34. BigML, Inc 34Topic Modelling
Topic Distribution
• Any given document is likely a mixture of the
modeled topics…
• This can be represented as a distribution of topic
probabilities
Intuition:
Will 2020 be
the year that
humans will
embrace
space
exploration
and finally
travel to Mars?
Topic: travel
cat shoe zebra
ball tree jump
pen asteroid
cable box step
cabinet yellow
plate flashlight…
word probability
travel 23,55 %
airplane 2,33 %
mars 0,003 %
mantle ϵ
… ϵ
11%
Topic: space
cat shoe zebra
ball tree jump
pen asteroid
cable box step
cabinet yellow
plate flashlight…
word probability
space 38,94 %
airplane ϵ
mars 13,43 %
mantle 0,05 %
… ϵ
89%
36. BigML, Inc 36Topic Modelling
Batch Topic Distribution
Unlabelled Data
Centroid Label
Unlabelled Data
topic 1
prob
topic 3
prob
topic k
prob
Clustering Batch Centroid
Topic Model
Text Fields
Batch Topic Distribution
…
38. BigML, Inc 38Topic Modelling
Tips
• Setting k
• Much like k-means, the best value is data specific
• Too few will agglomerate unrelated topics, too many will
partition highly related topics
• I tend to find the latter more annoying than the former
• Tuning the Model
• Remove common, useless terms
• Set term limit higher, use bigrams