Association Analysis

Question 1

a collection of one or more items

Answer 1

Itemset

Question 2

fraction of transaction that contain an itemset.

Answer 2

Support

Question 3

an itemset whose support is greater than or equal to minsup (minimum support) threshold.

Answer 3

Frequent Itemset

Question 4

an implication of the form X -> Y where X and Y are itemset.

Answer 4

Association Rule

Question 5

2 Rule Evaluation Metrics for Association Analysis

Answer 5

1. Support
2. Vector

Question 6

fraction of transaction that contain both X and Y

Answer 6

Support

{Milk, Bread} -> {Diaper}

Formula:
{Milk, Bread, Diaper} / # of List

In the example:
2 / 5 has {Milk, Bread, Diaper}

Support = 0.4

Question 7

measures how often items in Y appears in transaction that contain X.

Answer 7

Confidence

{Milk, Bread} -> {Diaper}

Formula:
{Milk, Bread, Diaper} / {Milk, Bread} (# of occurrence)

In the example:
2 / 3 (3 instances where {Milk, Bread} is in the list)

Confidence = 0.67

Question 8

Given a set of transactions, the goal of association rule mining is to find all rules having:
o Support >= minsup
threshold

  o Confidence >= 
   minconf threshold

Answer 8

Association Rule Mining Task

Question 9

an approach where we list all possible association rules, compute the support and confidence for each rule, prune rules that fail minsup and minconf threshold.

Answer 9

Brute Force Approach

Question 10

The 2 Step Approach in Mining Association Rules

Answer 10

1. Frequent Itemset Generation
2. Rule Generation

Question 11

generate all itemset whose support >= minsup

Answer 11

Frequent Itemset Generation

Question 12

3 Generation Strategies in Frequent Itemset Generation

Answer 12

• Reduce number of candidates
• Reduce number of transactions
• Reduce number of comparisons

Question 13

a principle that states if an itemset is frequent, then all of its subset must also be frequent.

Answer 13

Apriori Principle

Question 14

a process of scanning database of transaction to determining the support of each candidate itemset.

Answer 14

Candidate Counting

Question 15

This is where we store candidate to reduce number of candidates.

Answer 15

Hash Structure.

Question 16

Is an approach in mining association rules where we generate high confidence rules from each frequent itemset, where each rule is a binary partitioning of frequent itemset.

Answer 16

Rule Generation

Question 17

4 Factors Affecting Complexity in Association Analysis

Answer 17

1. Choice of minimum support threshold.
2. Dimensionality (# of items) of the dataset
3. Size of database
4. Average transaction width.