![Associationinfrequent
patternmining
[AprioriAlgorithm]
By Asha Singh and Shreea Bose](https://image.slidesharecdn.com/presentation1-210130162706/85/Association-in-Frequent-Pattern-Mining-1-320.jpg)
![Nowgenerationofstrongassociationrulecomesintopicture.
Forthatweneedtocalculateconfidenceofeachrule.
SO rules can be
● [I1^I2]=>[I3] //confidence = sup(I1^I2^I3)/sup(I1^I2) = 2/4*100=50%
● [I1^I3]=>[I2] //confidence = sup(I1^I2^I3)/sup(I1^I3) = 2/4*100=50%
● [I2^I3]=>[I1] //confidence = sup(I1^I2^I3)/sup(I2^I3) = 2/4*100=50%
● [I1]=>[I2^I3] //confidence = sup(I1^I2^I3)/sup(I1) = 2/6*100=33%
● [I2]=>[I1^I3] //confidence = sup(I1^I2^I3)/sup(I2) = 2/7*100=28%
● [I3]=>[I1^I2] //confidence = sup(I1^I2^I3)/sup(I3) = 2/6*100=33%
● So if minimum confidence is 50%, then first 3 rules can be
considered as strong association rules.
Itemset Support Count
I1,I2,I3 2
I1,I2,I5 2
Itemset Support Count
I1,I2 4
I1,I3 4
I1,I5 2
I2,I3 4
I2,I4 2
I2,I5 2](https://image.slidesharecdn.com/presentation1-210130162706/85/Association-in-Frequent-Pattern-Mining-22-320.jpg)
Association in Frequent Pattern Mining
- 1. Associationinfrequent patternmining [AprioriAlgorithm] By Asha Singh and Shreea Bose
- 2. TABLEOFCONTENTS WhatisFrequentPattern Analysis? ImportanceofFrequent PatternAnalysis BasicConceptandRules AprioriAlgorithm PseudoCodeand WorkingCode 01 02 04 05 03 Limitations 06 Conclusion 07
- 3. 01 WhatisFrequentPattern Analysis? It describes the task of finding the most frequent and relevant patterns in large datasets.
- 4. Definition Frequent Pattern Mining is a Data Mining subject with the objective of extracting frequent itemsets from a database.
- 5. ConceptofFrequentPatternAnalysis Pattern Series of data that repeats in a recognizable way. Can be study of Sales and Volume. Occurrence Enable us to predict the occurrence of a specific item based on various transactions. Relationship It plays a crucial role in mining associations, correlations, and many other innovative relationships among data.
- 6. Market Basket Analysis is the best example of Frequency Pattern Analysis. Here we try to find sets of products that are frequently bought together by different customers, so as to increase the sale in products. By applying algorithm on the sales we can find the pattern in which items are bought, like bread and milk here occurs thrice.
- 7. ImportanceofFrequent PatternAnalysis Where should we use this and why? 02
- 8. INBRIEF ● It aims at finding regularities in the shopping behavior of customers of supermarkets, mail-order companies, online shops. ● This method of analysis can be useful in evaluating data for various business functions and industries. ● To work with other businesses that complement your own, not competitors. For example, vehicle dealerships and manufacturers have cross marketing campaigns with oil and gas companies for obvious reasons. ● Each patient is represented as a transaction containing the ordered set of diseases, and which diseases are likely to occur simultaneously/sequentially can be predicted.
- 10. TermsassociatedwithPatternMining Support This says how popular an itemset is, as measured by the proportion of transactions in which an itemset appears. Lift This says how likely item Y is purchased when item X is purchased, while controlling for how popular item Y is. 01 02 03 Confidence This says how likely item Y is purchased when item X is purchased, expressed as {X -> Y}. This is measured by the proportion of transactions with item X, in which item Y also appears.
- 11. AssociationMining Twostepprocess GenerateRules These rules must satisfy minimum support and minimum confidence The aim is to discover associations of items occurring together more often than we expect from randomly sampling all the possibilities. Findfrequent itemsets ● Apriori Algorithm ● Fp Growth 01 03 02
- 12. 04 AprioriAlgorithm Given by R. Agrawal and R. Srikant in 1994 for finding frequent itemsets in a dataset for boolean association rule
- 13. AprioriAlgorithmandProperties All non-empty subset of frequent itemset must be frequent. The key concept of Apriori algorithm is its anti- monotonicity of support measure. We apply an iterative approach or level-wise search where k-frequent itemsets are used to find k+1 itemsets Name of the algorithm is Apriori because it uses prior knowledge of frequent itemset properties. Apriori assumes that all subsets of a frequent itemset must be frequent. If an itemset is infrequent, all its supersets will be infrequent.
- 15. Let’sworkonasimpleexample Tid ITEMS T1 I1,I2,I5 T2 I2,I4 T3 I2,I3 T4 I1,I2,I4 T5 I1,I3 T6 I2,I3 T7 I1,I3 T8 I1,I2,I3,I5 T9 I1,I2,I3 ● minimum support count is 2 ● minimum confidence is 60%
- 16. Let’sworkonasimpleexample Itemset Support Count I1 6 I2 7 I3 6 I4 2 I5 2 Itemset Support Count I1 6 I2 7 I3 6 I4 2 I5 2 Compare candidate set item’s support count with minimum support count (here min_support=2 if support_count of candidate set items is less than min_support then remove those items). This gives us itemset L1.
- 17. Let’sworkonasimpleexample Generate candidate set C2 using L1 (this is called join step). Condition of joining Lk-1 and Lk-1 is that it should have (K-2) elements in common. Itemset Support Count I1,I2 4 I1,I3 4 I1,I4 1 I1,I5 2 I2,I3 4 I2,I4 2 I2,I5 2 I3,I4 0 I3,I5 1 I4,I5 0 Tid ITEMS T1 I1,I2,I5 T2 I2,I4 T3 I2,I3 T4 I1,I2,I4 T5 I1,I3 T6 I2,I3 T7 I1,I3 T8 I1,I2,I3,I5 T9 I1,I2,I3
- 18. Let’sworkonasimpleexample Compare candidate (C2) support count with minimum support count(here min_support=2 if support_count of candidate set item is less than min_support then remove those items) this gives us itemset L2. Itemset Support Count I1,I2 4 I1,I3 4 I1,I5 2 I2,I3 4 I2,I4 2 I2,I5 2
- 19. Let’sworkonasimpleexample ● Generate candidate set C3 using L2 (join step). Condition of joining Lk-1 and Lk-1 is that it should have (K-2) elements in common. So here, for L2, first element should match. ● find support count of these remaining itemset by searching in dataset. ● Compare candidate (C3) support count with minimum support count(here min_support=2 if support_count of candidate set item is less than min_support then remove those items) this gives us itemset L3. Itemset Support Count I1,I2,I3 2 I1,I2,I5 2
- 20. Let’sworkonasimpleexample ● Generate candidate set C4 using L3 (join step). Condition of joining Lk-1 and Lk- 1 (K=4) is that, they should have (K-2) elements in common. So here, for L3, first 2 elements (items) should match. ● Check all subsets of these itemsets are frequent or not (Here itemset formed by joining L3 is {I1, I2, I3, I5} so its subset contains {I1, I3, I5}, which is not frequent). So no itemset in C4 ● We stop here because no frequent itemsets are found further
- 21. StrongAssociationandConfidence ● Strong Association Rules: rules whose confidence is greater than or equal to a confidence threshold value. Here the threshold value is 60% ● Confidence(A->B)=Support_count(A∪B)/Support_count(A) ● Itemset B is Coke, and Itemset A is {diapers, milk} so we want to find the probability that Coke exists in a transaction given that {diapers, milk} does. ● So the Confidence of {diapers, milk}→coke = 2/3 =0.667 ● {diapers, milk}→coke is a strong association rule because its confidence is 0.67 Ti d Items 1 Bread, Milk 2 Bread, Diaper, Beer, Eggs 3 Milk, Diaper, Beer, Coke 4 Bread, Milk, Diaper, Beer 5 Bread, Milk, Diaper, Coke
- 22. Nowgenerationofstrongassociationrulecomesintopicture. Forthatweneedtocalculateconfidenceofeachrule. SO rules can be ● [I1^I2]=>[I3] //confidence = sup(I1^I2^I3)/sup(I1^I2) = 2/4*100=50% ● [I1^I3]=>[I2] //confidence = sup(I1^I2^I3)/sup(I1^I3) = 2/4*100=50% ● [I2^I3]=>[I1] //confidence = sup(I1^I2^I3)/sup(I2^I3) = 2/4*100=50% ● [I1]=>[I2^I3] //confidence = sup(I1^I2^I3)/sup(I1) = 2/6*100=33% ● [I2]=>[I1^I3] //confidence = sup(I1^I2^I3)/sup(I2) = 2/7*100=28% ● [I3]=>[I1^I2] //confidence = sup(I1^I2^I3)/sup(I3) = 2/6*100=33% ● So if minimum confidence is 50%, then first 3 rules can be considered as strong association rules. Itemset Support Count I1,I2,I3 2 I1,I2,I5 2 Itemset Support Count I1,I2 4 I1,I3 4 I1,I5 2 I2,I3 4 I2,I4 2 I2,I5 2
- 23. PseudoCode
- 24. LIMITATIONS 06
- 25. LimitationsofAprioriAlgorithm Requires many database scans. Efficiency It is slower than FP Growth Algorithm FPGrowth To detect frequent pattern in size 100 i.e. v1, v2… v100, it have to generate 2^100 candidate itemsets Costlyandwastingoftime Time required to hold a vast number of candidate sets with much frequent itemsets, low minimum support or large itemsets Slow 02 01 03 04
- 26. Conclusion 07
- 27. ● The Association rule is very useful in analyzing datasets. ● The data is collected using barcode scanners in supermarkets. Such databases consists of a large number of transaction records which list all items bought by a customer on a single purchase. ● Apriori, while historically significant, suffers from a number of inefficiencies or trade-offs, which have spawned other algorithms. ● Later algorithms such as Max-Miner try to identify the maximal frequent item sets without enumerating their subsets, and perform "jumps" in the search space rather than a purely bottom-up approach.
- 28. ● https://www.youtube.com/watch?v=guVvtZ7ZClw ● http://people.cs.pitt.edu/~iyad/AR.pdf ● https://medium.com/@ciortanmadalina/an-introduction-to-frequent- pattern-mining-research-564f239548e ● https://www.geeksforgeeks.org/apriori-algorithm/ apriori system ● apriori slide ● https://www.youtube.com/watch?v=guVvtZ7ZClw ● https://arxiv.org/ftp/arxiv/papers/1403/1403.3948.pdf ● https://www.geeksforgeeks.org/frequent-item-set-in-data-set-association- rule-mining/ ● https://en.wikipedia.org/wiki/Apriori_algorithm RESOURCES
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