Transcript Sl1 - Maastricht University

Knowledge Discovery and
Data Mining
Evgueni Smirnov
Outline
• Data Flood
• Definition of Knowledge Discovery and
Data Mining
• Possible Tasks:
– Classification Task
– Regression Task
– Clustering Task
– Association-Rule Task
Data Flood
Trends Leading to Data Flood
• Moore’s law
– Computer Speed doubles every 18 months
• Storage law
– total storage doubles every 9 months
As a result:
• More data is captured:
– Storage technology faster and cheaper
– DBMS capable of handling bigger DB
Trends Leading to Data Flood
• More data is generated:
– Business:
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•
•
•
Supermarket chains
Banks,
Telecoms,
E-commerce, etc.
– Web
– Science:
•
•
•
•
astronomy,
physics,
biology,
medicine etc.
Consequence
• Very little data will ever be looked at by a
human, and thus, we need to automate the
process of Knowledge Discovery to make
sense and use of data.
Definition of Knowledge Discovery
• Knowledge Discovery in Data is non-trivial
process of identifying
–
–
–
–
valid
novel
potentially useful
and ultimately understandable patterns in data.
• from Advances in Knowledge Discovery and Data Mining, Fayyad,
Piatetsky-Shapiro, Smyth, and Uthurusamy, (Chapter 1), AAAI/MIT
Press 1996.
Related Fields
Machine
Learning
Visualization
Knowledge Discovery
Statistics
Databases
Knowledge-Discovery Methodology
Knowledge
Data Mining is searching for patterns of
interest in a particular representation.
Target
data
data
Selection
Processed
data
Transformed
data
Patterns
Interpretation
Evaluation
Data Mining
Transformation
Preprocessing & feature
selection
& cleaning
Data-Mining Tasks
•
•
•
•
Classification Task
Regression Task
Clustering Task
Association-Rule Task
Classification Task
• Given: a collection of instances (training set)
– Each instances is represented by a set of attributes, one of
the attributes is the class attribute.
• Find: a classifier for the class attribute as a function
of the values of other attributes.
• Goal: previously unseen instances should be
assigned a class as accurately as possible.
Example 1
Tid Refund Marital
Status
Taxable
Income Cheat
Refund Marital
Status
Taxable
Income Cheat
1
Yes
Single
125K
No
No
Single
75K
?
2
No
Married
100K
No
Yes
Married
50K
?
3
No
Single
70K
No
No
Married
150K
?
4
Yes
Married
120K
No
Yes
Divorced 90K
?
5
No
Divorced 95K
Yes
No
Single
40K
?
6
No
Married
No
No
Married
80K
?
60K
10
7
Yes
Divorced 220K
No
8
No
Single
85K
Yes
9
No
Married
75K
No
10
10
No
Single
90K
Yes
Training
Set
Learn
Classifier
Test
Set
Classifier
Example 2
• Fraud Detection
– Goal: Predict fraudulent cases in credit card
transactions.
– Approach:
• Use credit card transactions and the information on
its account-holder as attributes.
– When does a customer buy, what does he buy, how often
he pays on time, etc
• Label past transactions as fraud or fair transactions.
This forms the class attribute.
• Learn a model for the class of the transactions.
• Use this model to detect fraud by observing credit
card transactions on an account.
Regression Task
• Predict a value of a given continuous valued variable
based on the values of other variables, assuming a
linear or nonlinear model of dependency.
Examples:
• Predicting sales amounts of
new product based on
advertising expenditure.
• Predicting wind velocities as
a function of temperature,
humidity, air pressure, etc.
• Time series prediction of
stock market indices.
Clustering Task
• Given a set of data points, each having a set of
attributes, and a similarity measure among them,
find clusters such that:
– Data points in one cluster are more similar;
– Data points in separate clusters are less similar.
Intra-cluster distances
are minimized
Inter-cluster distances
are maximized
Example
• Market Segmentation:
– Goal: subdivide a market into distinct subsets
of customers where any subset may
conceivably be selected as a market target to be
reached with a distinct marketing mix.
– Approach:
• Collect different attributes of customers based on
their geographical and lifestyle related information.
• Find clusters of similar customers.
• Measure the clustering quality by observing buying
patterns of customers in same cluster vs. those from
different clusters.
Association-Rule Task
• Given a set of records each of which contain
some number of items from a given collection;
– Produce dependency rules which will predict
occurrence of an item based on occurrences of
other items.
TID
Items
1
2
3
4
5
Bread, Coke, Milk
Beer, Bread
Beer, Coke, Diaper, Milk
Beer, Bread, Diaper, Milk
Coke, Diaper, Milk
Rules Discovered:
Milk --> Coke
Diaper, Milk --> Beer
Example
• Supermarket shelf management.
– Goal: To identify items that are bought together
by sufficiently many customers.
– Approach: Process the point-of-sale data
collected with barcode scanners to find
dependencies among items.
– A classic rule -• If a customer buys diaper and milk, then he is very
likely to buy beer.
• So, don’t be surprised if you find six-packs stacked
next to diapers!
Course Overview
Processed
data
data
Selection
Monday:
Interpretation
Evaluation
Data Mining
Transformation
Preprocessing & feature
selection
& cleaning
BDM1: Decision Trees and Decision Rules (Kurt Driessens)
BDM2: Evaluation of Learning Models (Kurt Driessens)
S1: Regression Analysis (Georgi Nalbantov)
S2: Survival Analysis (Nasser Davarzani)
Course Overview
Processed
data
data
Selection
Interpretation
Evaluation
Data Mining
Transformation
Preprocessing & feature
selection
& cleaning
Tuesday:
BDM3: Instance learning and Bayesian learning (E. Smirnov)
BDM4: Feature Selection and Reduction; Clustering (Georgi Nalbantov)
ADM1: Transfer for Supervised-Learning Tasks (Haitham Bou Ammar)
Course Overview
Processed
data
data
Selection
Interpretation
Evaluation
Data Mining
Transformation
Preprocessing & feature
selection
& cleaning
Wednesday : BDM5: Association Rules (E. Smirnov)
ADM2: Ensemble Methods (E. Smirnov)