Data Mining

1. Association Analysis
with R Studio
9th week of
DATA MINING CLASS
Anik Nur Habyba, STP, MSi
anik@trisakti.ac.id

2.  Affinity Analysis
 Apriori Algorithm in R Studio
 FP Growth in R Studio
Association Rules with R Studio:
Expected Final Ability
Students can analyze, interpret data and information and make appropriate decisions
based on the association analysis approach (CPMK1, CPMK2, KUE, KKB).

3. Affinity Analysis
 Affinity analysis is the study of attributes or characteristics that “go together.”
 Methods for affinity analysis, also known as market basket analysis, seek to uncover
associations among these attributes; that is, it seeks to uncover rules for quantifying
the relationship between two or more attributes.
 Association rules take the form “If antecedent, then consequent,” along with a
measure of the support and confidence associated with the rule.
“If buy chips, then buy wine”

4. Examples of association tasks in
business and research include
 Investigating the proportion of subscribers to your company’s cell phone plan that
respond positively to an offer of a service upgrade;
 Examining the proportion of children whose parents read to them who are
themselves good readers;
 Predicting degradation in telecommunications networks; finding out which items in
a supermarket are purchased together, and which items are never purchased
together;
 Determining the proportion of cases in which a new drug will exhibit dangerous
side effects.
 Formulating the product or service design, by identifying the design elements of
popular product and service in the market

5. Data Representation for Market
Basket Analysis

6. SUPPORT AND CONFIDENCE
The support s for a particular association rule A ⇒ B is the proportion of transactions in D that
contain both A and B. That is,
The confidence c of the association rule A ⇒ B is a measure of the accuracy of the rule, as
determined by the percentage of transactions in D containing A that also contain B. In other words,
Strong rules are those that meet or surpass certain minimum support and confidence criteria

7. FREQUENT ITEMSETS
 An itemset is a set of items contained in I, and a k-itemset is an itemset
containing k items.
For example, {beans, squash} is a 2-itemset, and {broccoli, green peppers,
corn} is a 3-itemset, each from the vegetable stand set I.
 The itemset frequency is simply the number of transactions that contain the
particular itemset.
 A frequent itemset is an itemset that occurs at least a certain minimum
number of times, having itemset frequency≥𝜙.
For example, suppose that we set 𝜙=4.
Then itemsets that occur more than four times are said to be
frequent. We denote the set of frequent k-itemsets as Fk.

8. HOW CAN WE MEASURE THE
USEFULNESS OF ASSOCIATION RULES?
In general, association rules with lift values different from 1 will be more interesting and
useful than rules with lift values close to 1. Why are rules with lift values close to 1 not
useful?
The ratio being close to 1 implies that A and B are independent events, meaning
that knowledge of the occurrence of A does not alter the probability of the
occurrence of B. Such relationships are not useful from a data mining perspective,
and thus it makes sense that we prefer our association rules to have a lift value
different from 1.

9. Association rule steps:

10.  Generalized Rule Induction (GRI)
a. Discovers association rules in the data.
b. For example, customers who purchase razors and aftershave lotion are also
likely to purchase shaving cream.
c. GRI extracts rules with the highest information content based on an index that
that takes both the generality (support) and accuracy (confidence) of rules
into account.
d. GRI can handle numeric and categorical inputs, but the target must be
categorical.
 Apriori model
a. Extracts a set of rules from the data, pulling out the rules with the highest
information content.
b. Apriori requires that input and output fields all be categorical
Association rule algorithms are
supported:

11. Association rule algorithms are supported (2):
 CARMA model
a. The CARMA model offers build settings for rule support (support for both
antecedent and consequent) rather than just antecedent support.
 Sequence model
a. Discovers association rules in sequential or time-oriented data.
b. A sequence is a list of item sets that tends to occur in a predictable order.
c. For example, a customer who purchases a razor and aftershave lotion may
purchase shaving cream the next time he shops.

12. • https://rpubs.com/anikbibib/Arules

13. • https://www.researchgate.net/publication/331209652_An_affecti
ve_design_for_jenang_packaging_in_Indonesia
• https://www.ijeat.org/wp-
content/uploads/papers/v10i2/B19611210220.pdf