Data Mining, Apriori algorithm, mining the data

1. Jung Hoon Kim
N5, Room 2239
E-mail: junghoon.kim@kaist.ac.kr
2014.01.07
KAIST Knowledge Service Engineering
Data Mining Lab.
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2. Introduction
 Frequent pattern and association rule mining is one of
the few exceptions to emerge from machine learning
 Apriori algorithm
 AprioriTid algorithm
 AprioriAll algorithm
 FP-Tree algorithm
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3. Notation

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4. Principle
 downward closure property.
 If an itemset is frequenct,
then all of its subsets must
also be frequent
 if an itemset is not frequent,
any of its superset is never
frequent
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5. Apriori algorithm
 Pseudo code
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6. Example
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7. Discussion
 Too many database scanning makes high computation
 Need minsup & minconf to be specified in advance.
 Use hash-tree to store the candidate itemsets.
Sometimes it adapt trie-structure to store sets.
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8. AprioriTid

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9. AprioriTid
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10. AprioriTid
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11. AprioriTid
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12. FP-Growth
 To avoid scanning multiple database
 the cost of database is too high !!
 To avoid making lots of candidates
 in apriori algorithm, the bottleneck is generation of
candidate
 How can solve these problems?
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13. FP-Growth
 Algorithm was too simple
1. Scan the database once, find frequent 1-itemsets
(single item patterns)
2. Sort the frequent items in frequency descending
order, f-list(F-list = f-c-a-b-m-p)
3. Scan the DB again, construct the FP-tree
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14. FP-Growth Algorithm
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15. FP-Tree
 Scanning the transaction with TID=100
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16. FP-Tree
 Scanning the transaction with TID=200
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17. FP-Tree
 Final FP-Tree
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18. Mine a FP-Tree
forming conditional pattern bases
II. constructing conditional FP-trees
III. recursively mining conditional FP-trees
I.
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19. Conditional pattern base
 frequent itemset as a co-occurring
suffix pattern
 for example
 m : <f, c, a> : support / 2
 m : <f,c,a,b> : support / 1
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20. Conditional pattern tree
 {m}’s conditional pattern tree
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21. Pseudo Code
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22. Conclusion
 In data mining, association rules are useful for analyzing
and predicting customer behavior. They play an
important part in shopping basket data analysis, product
clustering, catalog design and store layout.
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23. Thank you
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