# Nm itemsets

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ITS-632 Intro to Data Mining
Dr. Patrick Haney
Dept. of Information Technology &
School of Computer and Information Sciences
University of the Cumberlands
Chapter 6 Assignment
[
Data Mining Association Analysis: Basic Concepts and Algorithms Assignment
1) Explain the components and the use of the Mining Association Rules.
The association rules in data mining are made up of two main components. These
components are antecedent and consequent. An antecedent is within the data while a
consequent is found within or combined with the antecedent. Association rules in data mining
are applied and created in order to search for patterns within the data.
2) List and Explain the three “Frequent Itemset Generation Strategies”?
The first is reducing the number of candidates. In this case, pruning strategies are applied to reduce
the size of M within the data. The second is targeted at the reduction of the number of comparisons
within a data set. The need to match every candidate against every transaction is eliminated. The third
strategy is the use of the apriori principle. The apriori principle basically is the basis for the above two
mentioned strategies.
3) In Rule Generation, how do you efficiently generate rules from frequent itemset?
In rule generation, the level of confidence is considered. The highest level of confidence
is the one that is applied to generate the rules in itemsets that are frequent. Binary partitioning
is also used in every frequent itemset. Binary partitioning is used to generate high confidence
rules. The rule in this case is the partitioning of the itemsets.
4) In Support-Based Pruning: Most of the _________apriori__________ algorithms use support
measure to ___generate___ rules and itemsets. (Fill in the blanks)

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ITS-632 Intro to Data Mining Dr. Patrick Haney Dept. of Information Technology & School of Computer and Information Sciences University of the Cumberlands Chapter 6 Assignment [ Data Mining Association Analysis: Basic Concepts and Algorithms Assignment 1) Explain the components and the use of the Mining Association Rules. The association rules in data mining are made up of two main components. These components are antecedent and consequent. An antecedent is within the data while a consequent is found within or combined with the antecedent. Association rules in data mining are applied and cr ...
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