Homework $05-$Data Mining and Text Mining

CLO$5.$ Assess the significance of mined frequent patterns, as well as the association rules derived from them by using rule and pattern assessment measures and employing algorithms to test for the statistical significance of rules and patterns. $($Evaluation$)$

Homework $5$ directly aligns with the fifth course learning outcome $($CLO $5),$ which focuses on assessing the significance of mined frequent patterns and association rules using rule evaluation measures and statistical thresholds. Through tasks such as identifying frequent itemsets, generating association rules, and calculating metrics like support and confidence, students evaluate the strength and significance of patterns derived from datasets. By employing algorithms such as Apriori, ECLAT, and FPGrowth, the homework ensures students not only understand the mechanics of mining frequent patterns but also critically assess their importance and applicability. This comprehensive approach allows students to demonstrate their ability to evaluate mined rules and patterns effectively, fulfilling the evaluation component of CLO $5.$ Homework $05-$Data Mining and Text Mining

Question $1(25$ points$)$: Apply the Apriori algorithm to generate frequent itemsets and association rules. Show your calculations for each step clearly.

Consider the following transaction dataset:

$1.$ Generate Frequent Itemsets:

$-$ Use the Apriori algorithm with a minimum support count of $2.$

$-$ List all frequent $1-$itemsets, $2-$itemsets, and $3-$itemsets.

$2.$ Generate Association Rules:

$-$ Using the frequent itemsets generated above, generate all association rules with a minimum confidence of $\backslash (60\backslash \%\backslash ).$

$-$ For each rule, calculate the support, confidence, and lift.

$3.$ Answer the following questions:

$-$ Which item is most frequently purchased?

$-$ Are there any strong associations $($confidence $>80\backslash \%)$ between items? If yes, describe them. Homework $05-$Data Mining and Text Mining

Question $2(25$ points$)$: Apply the ECLAT algorithm to generate frequent itemsets based on a given transaction dataset. Represent the vertical format clearly in your solution. Show step$-$by$-$step intersections for $2-$itemsets and $3-$itemsets.

Consider the following transaction dataset:

$1.$ Generate Frequent Itemsets:

$-$ Use the ECLAT algorithm with a minimum support count of $2.$

$-$ Represent the dataset in vertical format $($list of transaction IDs for each item$).$

$-$ Use the intersection of transaction IDs to generate:

$-$ Frequent $1-$itemsets

$-$ Frequent $2-$itemsets

$-$ Frequent $3-$itemsets

$2.$ Answer the following questions:

$-$ Which item has the highest frequency $($support$)?$

$-$ Are there any $3-$itemsets with the given minimum support? Homework $05-$Data Mining and Text Mining

Question $3(25$ points$)$: Apply the FP$-$Growth algorithm to identify frequent itemsets from a transaction dataset. Show a clear representation of the FP$-$tree, for the dataset using a minimum support count of $2,$ Step$-$by$-$step construction of conditional FPtrees for generating frequent patterns, and a list of all frequent itemsets with their support counts.

Given the following transaction dataset: Question $4(25$ points$)$: Apply the FP$-$Growth algorithm to identify frequent itemsets from a transaction dataset. Show a clear representation of the FP$-$tree, Step$-$by$-$step construction of conditional FP$-$trees for generating frequent patterns, and a list of all frequent itemsets with their support counts.

Given the following transaction dataset:

Tasks:

$1.$ Construct the FP$-$tree for the dataset using a minimum support count of $2.$

$2.$ Use the FP$-$Growth algorithm to generate:

$-$ Frequent $1-$itemsets

$-$ Frequent $2-$itemsets

$-$ Frequent $3-$itemsets

$3.$ For the conditional FP$-$trees, show the process for generating the frequent patterns for the item "Diaper".