1. Data Mining II: Association Rule mining & Classification
Jagdish Gangolly
State University of New York at Albany
Acc 522 Fall 2001 Jagdish S. Gangolly
11/15/2018

2. Data Mining II Attribute-Oriented Induction Mining association rules
Mining single-dimensional boolean association rules
Classification
Acc 522 Fall 2001 Jagdish S. Gangolly
11/15/2018

3. Attribute-Oriented Induction I
Steps:
Original query (in DMQL)
specify the database to be mined
specify relevant attributes
specify the relation to be mined
specify the concept in the hierarchy
Transformation of DMQL to relational query whose execution yields initial working relation.
Acc 522 Fall 2001 Jagdish S. Gangolly
11/15/2018

4. Attribute-Oriented Induction II
Attribute removal/generalisation:
removal rule: remove attribute if
no generalisation operator on the attribute (large set of attribute values, but nogeneralisation operator)
higher level concepts in the hierarchy expressed in terms of other attributes (address example)
generalisation rule: if there are many attribute values and there are generalisation operators, use them
attribute generalisation threshold control
Acc 522 Fall 2001 Jagdish S. Gangolly
11/15/2018

5. Basic Algorithm for Attribute-Oriented induction
Input: Relational database, DMQL query, a list
of attributes, a set of concept hierarchies,
attribute generalisation thresholds
Output: a Prime generalised relation
Method:
Collect task-relevant data into a working relation: get W
Collect statistics on the working relation
Derive the prime relation P.
Acc 522 Fall 2001 Jagdish S. Gangolly
11/15/2018

6. Mining association rules I
Some examples:
Market basket analysis: analysing customer buying habits
Intrusion detection by analysing user habits
Acc 522 Fall 2001 Jagdish S. Gangolly
11/15/2018

7. Mining association rules II
Basic concepts:
Set of items I
Task-relevant data D consisting of database transactions T  I
An association rule is an implication of the form
A  B
where A  I, B  I, A  B = 
support(A  B) = P(AB)
confidence(A  B ) = P(B/A)
Acc 522 Fall 2001 Jagdish S. Gangolly
11/15/2018

8. Mining association rules II
Classification of association rules:
Based on types of values
Boolean computer  financial-management-software
Quantitative association rule age(X, “30..39”)  income(X, “42K..48K”)  buys(X, “financial-management-software”)
Based on dimensions of data involved in the rule buys(X, “computer”)  buys(X, “financial-management-software”)
Acc 522 Fall 2001 Jagdish S. Gangolly
11/15/2018

9. Mining association rules III
Based on levels of abstraction age(X, “30..39”)  buys(X, “laptop”) age(X, “30..39”)  buys(X, “computer”)
Acc 522 Fall 2001 Jagdish S. Gangolly
11/15/2018

10. Mining single-dimensional boolean association rules I
Apriori algorithm for finding frequent itemsets
Apriori property: (All nonempty subsets of a frequent itemset must also be frequent). If P(I) < min_sup, then for any item A, P(IA) < min_sup
Steps:
Join step: A set of candidate k-itemsets, denoted by Ck , generated by joining Lk-1 with itself.
Prune step: Prune Ck
Example 6-1 (p.232)
Acc 522 Fall 2001 Jagdish S. Gangolly
11/15/2018

11. Classification I Supervised learning
Training data
Test data
Training data is analysed to derive classification rules; the test data are used to estimate the accuracy of classification rules
Unsupervised learning or clustering
Acc 522 Fall 2001 Jagdish S. Gangolly
11/15/2018

12. Classification II Preliminary steps: Comparison/evaluation of methods:
data cleaning (reduction of noise, missing values, etc.)
relevance analysis (feature selection)
data transformation (generalisation, normalisation)
Comparison/evaluation of methods:
Predictive accuracy
speed
Robustness
Scalability
Interpretability
Acc 522 Fall 2001 Jagdish S. Gangolly
11/15/2018