1. Targeted Association Mining in Time-Varying Domains
Project proposal Fall 2016
Jennifer Lavergne

2. Project Goals Phase 1: Modify Min-Max Itemset Tree Code
Modify itemset tree code supplied for the project to find emerging and declining patters.
Phase 2: Prepare datasets for testing.
Datasets cleaned but need to be separated into time intervals
Phase 3: Emerging/Declining pattern mining tests.
Tests on real world and synthetic datasets.

3. Background

4. Association Mining
Large repositories of data containing hidden gems of information.
Rarer information is harder to find.
Find interesting correlations:
Items selling together
Contributors to fatal accidents
Symptoms for rare diseases
(Agrawal et al)

5. Association Rules
Association rule - an implication {X ⇒ Y, support, confidence}. Where X and Y are subsets of the itemset I and X∩Y = Ø
Example: {{bread, milk} ⇒ {cheese}, 30%, 75%}
Support = #occurrences of I in database/#rows in database
Minsup – The minimum support threshold for an itemset I to be considered frequent
Confidence = Support(X ⋃ Y)/Support(X) for itemset I = X ⋃ Y.
Minconf – a user specified threshold that indicates the interestingness of a candidate rule I: conf(I) > minconf

6. Itemset Trees
A data structure which aids in users querying for a specific itemset and it’s support: Targeted Association Mining
Item mapped to numeric values: {bread} = {1}, {cheese} = {2}
Numbers must be in ascending order within the itemset
Ex: I = {1, 2, 56, 120}
Note: Can be used to find all or specific
rules within a dataset.

7. Why Emerging?
By the time the data is analyzed, the value of the knowledge has greatly depreciated.
Velocity: Time to action vs. Value (Hackathorn, 2002)

8. Time Stream Data
Time stream – A stream of time sensitive data on which the algorithm will mine patterns.
Time window – A window delineating the current “interesting” time frame made up of n time steps.
Time step – An increment of time in which the time windows progress forward.

9. Time Stream Data: Moving Forward
Current time step:
Move forward 1 time step:

10. Dynamic Data Mining: Pattern Types
Time

11. Project Description

12. Phase 1: Itemset Tree Code
Modify existing code or write your own:
Process queries over time streams
Find emerging and declining patterns
Supports stored in a support array equal to the number of time steps
Read papers:
Itemset trees
Ordered Min-Max Itemset Tree
Dyn-TARM

13. Phase 2: Preparing datasets
For real world data:
Separate into time steps based upon dates in the data
For synthetic data:
Divide data into equal parts to create time steps

14. Phase 3: Tests (both datasets)
Question 1: What effect does modifying the input measures have? (0 < minSup, minConf < 1, 0< α < 100 time step size, and time window size.)
Question 2: What is the trend of each pattern over the time stream?
Is their emergence/declining cyclic or maintained all through?
Question 3: What is the distribution of emergence/declining patterns over each dataset?

15. Jennifer Lavergne jjslavergne@louisiana.edu
Questions?
Jennifer Lavergne