Frequent itemset mining methods


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Frequent itemset mining methods Algorithms & Solved Examples

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Frequent itemset mining methods

  1. 1. Frequent Item-set MiningMethodsPrepared By- Mr.Nilesh Magar
  2. 2. Data Mining:Data mining is the efficient discovery ofvaluable, non obvious information from alarge collection of data.Prepared By- Mr.Nilesh Magar
  3. 3.  Most important concepts in Data-mining Item-set & frequent item-set: Market Basket modelFrequent Item-set:Prepared By- Mr.Nilesh Magar
  4. 4. Example Of Market basket Model:B1 = {m, c, b} B2 = {m, p, j} B3 = {m, b} B4 = {c, j}B5 = {m, p, b} B6 = {m, c, b, j} B7 = {c, b, j} B8 = {b, c}Suppose Min support =3Frequent item-sets: {m:5}, {c:5}, {b:6}, {j:4}, {m, b:4}, {c,b:4}, {j, c:3}.Prepared By- Mr.Nilesh Magar
  5. 5. Association Rule: Medical diagnosis dataset-symptoms and illness. A rule is define as an implication of the form X Y where X,YI (Items). Or in other words: if { i1, i2,…, ik} j, means: if abasket contains all of i1,…, ik then it is likely to Contain j. The probability of finding Y for us to accept this rule is calledthe confidence of the rule. Conf(X Y)=SUPP(X U Y)/SUPP(X) {m,b}c ::: Confidence = 2/4 = 50% Thus Association mining is 2 step Process:Find all frequent item-sets:Generate Strong association rules from frequent item-setPrepared By- Mr.Nilesh Magar
  6. 6. The Apriori algorithm Mining frequent item-set for Boolean association rule Prior knowledge Iterative approach known as level-wise search k-item-sets are used to explore (k+1)-item-sets One full scan of the database required to find lK , L1->Items with Min Support. L2-> generating 2-item-set etc.Prepared By- Mr.Nilesh Magar
  7. 7.  Two steps:Joinfinding Lk, a set of candidate k-itemsets is generatedby joining Lk-1 with itselfPruneTo reduce the size of Ck the Apriori property is used:if any (k-1) subset of a candidate k-itemset is not inLk-1, then the candidate cannot be frequent either,soit can be removed from Ck. – subset testing.Prepared By- Mr.Nilesh Magar
  8. 8. Join & pruneStepPrepared By- Mr.Nilesh Magar
  9. 9. Example:TID List of item_IDsT100 I1, I2, I5T200 I2, I4T300 I2, I3T400 I1, I2, I4T500 I1, I3T600 I2, I3T700 I1, I3T800 I1, I2, I3, I5T900 I1, I2, I3Prepared By- Mr.Nilesh Magar
  10. 10. Prepared By- Mr.Nilesh Magar
  11. 11.  Scan D for count of each candidate C1: I1 – 6, I2 – 7, I3 -6, I4 – 2, I5 - 2 Compare candidate support count with minimum support count (min_sup=2) L1: I1 – 6, I2 – 7, I3 -6, I4 – 2, I5 - 2 Generate C2 candidates from L1 and scan D for count of each candidate C2: {I1,I2} – 4, {I1, I3} – 4, {I1, I4} – 1, … Compare candidate support count with minimum support count L2: {I1,I2} – 4, {I1, I3} – 4, {I1, I5} – 2, {I2, I3} – 4, {I2, I4} - 2, {I2, I5} – 2 Generate C3 candidates from L2 using the join and prune steps: Join: C3=L2xL2={{I1, I2, I3}, {I1, I2, I5}, {I1, I3, I5}, {I2, I3, I4}, {I2, I3, I5}, {I2, I4,I5}} Prune: C3: {I1, I2, I3}, {I1, I2, I5} Scan D for count of each candidate C3: {I1, I2, I3} - 2, {I1, I2, I5} – 2 Compare candidate support count with minimum support count L3: {I1, I2, I3} – 2, {I1, I2, I5} – 2 Generate C4 candidates from L3 C4=L3xL3={I1, I2, I3, I5} This itemset is pruned, because its subset {{I2, I3, I5}} is not frequent => C4=nullPrepared By- Mr.Nilesh Magar
  12. 12. Generating association rules fromfrequent item-sets: from Slide 5Finding the frequent item-sets fromtransactions in a database DGenerating strong association rules:Confidence(A=>B)=P(B|A)=support_count(AUB)/support_count(A)support_count(AUB) – number of transactionscontaining the itemsets AUBsupport_count(A) - number of transactions containingthe itemsets APrepared By- Mr.Nilesh Magar
  13. 13.  Example: lets have l={I1, I2, I5} The nonempty subsets are {I1, I2}, {I1, I5}, {I2, I5}, {I1}, {I2}, {I5}. Generating association rules:I1 and I2=>I5 conf=2/4=50%I1 and I5=>I2 conf=2/2=100%I2 and I5=> I1 conf=2/2=100%I1=>I2 and I5 conf=2/6=33%I2=>I1 and I5 conf=2/7=29%I5=>I1 and I2 conf=2/2=100%If min_conf is 70%, then only the second, third and last rules aboveare output.Prepared By- Mr.Nilesh Magar
  14. 14. Advantages & Disadvantages: Adv: 1) Uses Large item-setProperty 2) Easily parallelized 3) Easy to implementDis-Adv:1) Assumestransactiondatabase ismemory residentRequires up to ‘m’database scan.Prepared By- Mr.Nilesh Magar
  15. 15. Mining Frequent Itemsets withoutcandidate generationThe candidate generate and test methodIt may need to generate a huge number ofcandidate setsIt may need to repeatedly scan the databaseand check a large set of candidates by patternmatchingFrequent-pattern growth method(FP-growth) – frequent pattern tree(FP-tree)Prepared By- Mr.Nilesh Magar
  16. 16. Example:TID List of item_IDsT100 I1, I2, I5T200 I2, I4T300 I2, I3T400 I1, I2, I4T500 I1, I3T600 I2, I3T700 I1, I3T800 I1, I2, I3, I5T900 I1, I2, I3Prepared By- Mr.Nilesh Magar
  17. 17. Step-1:Item CountI1 6I2 7I3 6I4 2I5 2Step-2:Arrange Transaction in descending orderTID List of item(Before)List of item(After)T100 I1, I2, I5 I2,I1,I5T200 I2, I4 I2,I4T300 I2, I3 I2,I3T400 I1, I2, I4 I2,I1,I4T500 I1, I3 I1,I3T600 I2, I3 I2,I3T700 I1, I3 I1,I3T800 I1, I2, I3, I5 I2,I1,I3,I5T900 I1, I2, I3 I2,I1,I3Prepared By- Mr.Nilesh Magar
  18. 18. FP-TREEPrepared By- Mr.Nilesh Magar
  19. 19. Item ConditionalPattern BaseConditionalFP-treeFrequent PatternGeneratedI5 {{I2, I1:1}, {I2,I1, I3:1}}(I2:2, I1:2) {I2, I5:2}, {I1,I5:2}, {I2, I1, I5:2}I4 {{I2, I1:2},{I2:1}}(I2:2) {I2, I4:2}I3 {{I2, I1:2},{I2:2}, {I1:2}}(I2:4, I1:2),(I1:2),{I2, I3:4}, {I1,I3:4}, {I2, I1, I3:2}I1 {{I2:4}} (I2:4) {I2, I1:4}Prepared By- Mr.Nilesh Magar
  20. 20. Mining frequent itemsets using verticaldata format Transforming the horizontal data format of thetransaction database D into a vertical dataformat:Itemset TID_setI1 {T100, T400, T500, T700, T800, T900}I2 {T100, T200, T300, T400, T600, T800, T900}I3 {T300, T500, T600, T700, T800, T900}I4 {T200, T400}I5 {T100, T800}Prepared By- Mr.Nilesh Magar
  21. 21. Example For PracticePrepared By- Mr.Nilesh Magar
  22. 22. Minimum support threshold is 3Prepared By- Mr.Nilesh Magar
  23. 23. Prepared By- Mr.Nilesh Magar
  24. 24. T List of item(After)T1 f,c,a,m,pT2 f,c,a,b,mT3 f,bT4 c,b,pT5 f,c,a,p,mPrepared By- Mr.Nilesh Magar
  25. 25. {}f:4 c:1b:1p:1b:1c:3a:3b:1m:2p:2 m:1Header TableItem frequency headf 4c 4a 3b 3m 3p 3FP-Growth ExamplePrepared By- Mr.Nilesh Magar
  26. 26. FP-Growth ExampleEmptyEmptyf{(f:3)}|c{(f:3)}c{(f:3, c:3)}|a{(fc:3)}aEmpty{(fca:1), (f:1), (c:1)}b{(f:3, c:3, a:3)}|m{(fca:2), (fcab:1)}m{(c:3)}|p{(fcam:2), (cb:1)}pConditional FP-treeConditional pattern-baseItemPrepared By- Mr.Nilesh Magar
  27. 27. FP-Tree Algorithm:Input: DB, min_supportOutput: FP-Tree1. Scan DB & count all frequent items.2. Create null root & set as current node.3. For each Transaction T Sort T’s items. For each sorted Item I Insert I into tree as a child of current node. Connect new tree node to header list.Prepared By- Mr.Nilesh Magar
  28. 28. FP- Growth Algorithm:Prepared By- Mr.Nilesh Magar
  29. 29. Adv. & disAdv. Of FP- Growth:Adv:1) Only 2 Passes Over Data-set2) No Candidate Generation3) Much Faster Than AprioriDisAdv:• FP-Tree may not fit in memory.• FP-Tree is expensive to buildPrepared By- Mr.Nilesh Magar
  30. 30. Subjects1) U.M.L.2) P.P.L.3) D.M.D.W.4) O.S.5) Programming Languages6) RDBMSMr. Nilesh MagarLecturer at MIT, Kothrud, Pune.9975155310.Prepared By - Mr. Nilesh Magar
  31. 31. Thank YouPrepared By - Mr. Nilesh Magar