1. Stratified Sampling for Data Mining on the Deep Web
Tantan Liu, Fan Wang, Gagan Agrawal
{liut, wangfa
Dec. 16, 2010

2. Outline Introduction Background Knowledge Basic Formulation
Association Rule Mining
Differential Rule Mining
Basic Formulation
Main Technical Approach
A Greedy Stratification Method
Experiment Result
Conclusion

3. Introduction Deep Web Data mining on the deep web
Query interface vs. backend database
Input attribute vs. Output attribute
Data mining on the deep web
High level summary of the data
Challenge
Databases cannot be accessed directly
Sampling
Deep web querying is time consuming
Efficient Sampling Method

4. Background Knowledge-Association Rule Mining
Aim: co-occurrence patterns for items
Frequent Itemset: Support of the itemset is larger than a threshold
Rule:
is a frequent itemset
Confidence is larger than threshold

5. Background Knowledge-Differential Rule Mining
Aim: differences between two deep web data sources
E.g. Price of the same hotels on two web sites
Identical attributes vs. Differential attributes
Same vs. different values
Rule:
X: Frequent itemset composed of identical attributes
t: differential or target attribute
D1, D2: data sources

6. Basic Formulation-Problem Formulation
Two step sampling procedure
A pilot sample
Randomly drawn from the deep web
Interesting rules are identified
Additional sample
Verify identified rules
Association rules and differential rules
Sampling more data records satisfying X
X only contains input attributes – easy
X contains output attributes
Randomly sampling ? not efficient!
how?

7. Basic Formulation-Problem Formulation in Detail
Considering rules with
A single output attribute in the left hand
Association Rule
Estimate or,
Differential Rule
Estimate mean of given A=a
Goal – sampling
High estimation accuracy
Low sampling cost

8. Basic Formulation-Stratified Sampling
Sampling separately from strata
Heterogeneous across strata & homogenous within stratum
Estimating mean value of :
: size, and sampled mean value
Association Rule Mining
: whether an itemset is contained in a transaction
If an itemset is contained in a transaction,
Differential Rule Mining
:the value of target attribute

9. Background-Neymann Allocation
Sample Allocation
Determining sample size for each stratum
Fixed sum of sample size
Neymann Allocation
Minimizing variance of the stratified sampling
Problem of application in Deep Web
The probability of A = a in each stratum is not considered
Possible large sampling cost
Sampling cost: number of queries submitted to the deep web

10. Sampling Cost Sampling Cost on the Deep web Integrated Cost
Aim: obtain data records with
Sampling Cost:
: number of data records with
: probability of finding a data record with
Integrated Cost
Combing sampling cost and estimation variance
Two adjustable weights

11. Main technical Approach –Stratification Process
Stratification by a tree on the query space
A top-down construction manner
Best split to create child nodes
Input attribute with the smallest integrated cost
The splitting process stops
Integrated cost at each leaf node is small
Leaf nodes: final strata for sampling

12. Experiment Result Data Set: US census Two Metrics
The income of US households from 2008 US Census
40,000 data records
7 categorical and 2 numerical attributes
Two Metrics
Variance of Estimation
Sampling Cost

13. Experiment Result-Settings
Five sampling procedures
Four different weights for variance and sampling cost
Full_Var:
Var7 :
Var5 :
Var3 :
Rand : simple random sampling

14. Experiment Result – Variance of Estimation
Association Rule Mining
Increase of variance of estimation by decreasing
Random Sampling has higher estimation of variance

15. Experiment Result – Sampling Cost
Association Rule Mining
Decrease of sampling cost by decreasing
Random Sampling has higher sampling cost

16. Conclusion Stratified sampling for data mining on the deep web
Considering estimation accuracy and sampling cost
A tree model for the relation between input attributes and output attributes
A greedy stratification to maximally reduce an integrated cost metric
Our experiments show that
Higher sampling accuracy and lower sampling cost compared with simple random sampling
Reducing sampling costs by trading-off a fraction of estimation error

17. Questions & Comments?