The document discusses implementing the Apriori algorithm for association rule mining using the Weka data mining tool. It describes Apriori as a classical bottom-up algorithm for mining frequent itemsets and relevant association rules from transactional databases. It also outlines how to create a sample dataset in Excel, convert it to ARFF format, load it into Weka, apply the Apriori algorithm to generate association rules, and interpret the results.

1. ASSOCIATION RULE MINING
USING WEKA
PREPARED BY
TAZEEN TASNEEM
TABEEN TASNEEM

2. A-PRIORI ALGORITHM
• A classical algorithm.
• Used for mining frequent item sets and relevant association rules.
• Uses a “bottom up" approach.
• Devised to operate on a database containing a lot of transactions.
• Produces association rules.
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3. ATTRIBUTE TYPES IN A-PRIORI
For running a-priori algorithm all attribute type must be one of these –
 Nominal
 Binary
 Unary
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4. ASSOCIATION RULE
• A prominent and well-explored method for determining relations among variables in large databases.
• Helps to uncover relationships between seemingly unrelated data in a relational database.
• It has two parts –
 Antecedent (if)
 Consequent (then)
• Example –
Let us consider an association rule be
{Onion, Potato} => {Burger}
which means that if onion and potato are bought, customers also buy a burger.
• Created by analyzing data for frequent if/then patterns and using the criteria support and confidence to
identify the most important relationships.
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5. SUPPORT
• The support of an itemset X, supp(X) is the proportion of transaction in the database in which the item
X appears. It signifies the popularity of an itemset.
• 𝑠𝑢𝑝𝑝 𝑋 =
𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑡𝑟𝑎𝑛𝑠𝑎𝑐𝑡𝑖𝑜𝑛 𝑖𝑛 𝑤ℎ𝑖𝑐ℎ 𝑋 𝑎𝑝𝑝𝑒𝑎𝑟𝑠
𝑇𝑜𝑡𝑎𝑙 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑡𝑟𝑎𝑛𝑠𝑎𝑐𝑡𝑖𝑜𝑛𝑠
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6. CONFIDENCE
• Signifies the likelihood of item Y being purchased when item X is purchased.
• 𝑐𝑜𝑛𝑓 𝑋 → 𝑌 =
𝑠𝑢𝑝𝑝(𝑋 ∪ 𝑌)
𝑠𝑢𝑝𝑝(𝑋)
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7. AVAILABLE TOOLS
More popular tools used for data mining are –
• Weka
• Keel
In this presentation, we will use WEKA data mining tool.
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8. WHAT IS WEKA
• Waikato Environment for Knowledge Analysis (Weka).
• A collection of machine learning algorithms for data mining tasks.
• Contains tools for data –
 pre-processing
 classification
 Regression
 Clustering
 association rules
 visualization.
• An open source software issued under the GNU General Public License.
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9. DATASET IN WEKA
• Data set can be -
 CREATED
 DOWNLOAED
• For this presentation, we have created our own dataset using Microsoft Excel
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10. CREATING DATASET IN MICROSOFT EXCEL
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11. CREATING .CSV FILE
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12. CREATING .ARFF FILE
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13. WEKA INTERFACE
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14. LOADING (.ARFF) FILE IN WEKA
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15. APPLYING ASSOCIATION RULE
In WEKA, a-priori algorithm is default association rule.
Before running a-priori algorithm we have checked if all attributes are nominal or binary or unary.
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16. RUNNING A-PRIORI ALGORITHM
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17. RUNNING A-PRIORI ALGORITHM
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18. ASSOCIATOR OUTPUT
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19. INTERPRETATION OF RULES
Let us interpret our first rule where the rule is –
age = aged 5 ==> purchase = willBuy 5
Defines, if age = aged, then there are 5 incidents where purchase = willBuy.
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20. GRAPHICAL REPRESENTATION
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24. JITTER
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• Jitter function –
 adds artificial random noise to the coordinates of the plotted points
 spread the data out a bit.