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Association Mining

Association Mining. Data Mining Spring 2012. Transactional Database. Transactional Database Transaction – A row in the database i.e.: {Eggs, Cheese, Milk}. Items and Itemsets. Item = {Milk}, {Cheese}, {Bread}, etc. Itemset = {Milk}, {Milk, Cheese}, {Bacon, Bread, Milk}

vernon-kidd
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Association Mining

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  1. Association Mining Data Mining Spring 2012

  2. Transactional Database • Transactional Database • Transaction – A row in the database • i.e.: {Eggs, Cheese, Milk}

  3. Items and Itemsets • Item = {Milk}, {Cheese}, {Bread}, etc. • Itemset = {Milk}, {Milk, Cheese}, {Bacon, Bread, Milk} • Doesn’t have to be in the dataset • Can be of size 1 – n

  4. The Support Measure

  5. Support Examples Support({Eggs}) = 3/5 = 60% Support({Eggs, Milk}) = 2/5 = 40%

  6. Minimum Support Minsup– The minimum support threshold for an itemset to be considered frequent (User defined) Frequent itemset – an itemset in a database whose support is greater than or equal to minsup. Support(X) >minsup = frequent Support(X) < minsup = infrequent

  7. Minimum Support Examples Minimum support = 50% Support({Eggs}) = 3/5 = 60%  Pass Support({Eggs, Milk}) = 2/5 = 40%  Fail

  8. Association Rules

  9. Confidence Example 1 {Eggs} => {Bread} Confidence = sup({Eggs, Bread})/Sup({Eggs}) Confidence = (1/5)/(3/5) = 33%

  10. Confidence Example 2 {Milk} => {Eggs, Cheese} Confidence = sup({Milk, Eggs, Cheese})/sup({Milk}) Confidence = (2/5)/(3/5) = 66%

  11. Strong Association Rules Minimum Confidence– A user defined minimum bound on confidence. (Minconf) Strong association rule – a rule X=>Y whose conf>minconf. - this is a potentially interesting rule for the user. Conf(X=>Y) >minconf = strong Conf(X=>Y) < minconf = uninteresting

  12. Minimum Confidence Example Minconf = 50% {Eggs} => {Bread} Confidence = (1/5)/(3/5) = 33% Fail {Milk} => {Eggs, Cheese} Confidence = (2/5)/(3/5) = 66%  Pass

  13. Association Mining Association Mining: - Finds strong rules contained in a dataset from frequent itemsets. Can be divided into two major subtasks: 1. Finding frequent itemsets 2. Rule generation

  14. Transactional Database Revisited • Some algorithms change items into letters or numbers • Numbers are more compact • Easier to make comparisons

  15. Basic Set Logic Subset – a subset itemset X is contained in an itemset Y. Superset – a superset itemset Y contains an itemset X. example: X = {1,2} Y = {1,2,3,5} Y X

  16. Apriori • Arranges database into a temporary lattice structure to find associations • Apriori principle – 1. itemsets in the lattice with support < minsupwill only produce supersets with support < minsup. 2. the subsets of frequent itemsets are always frequent. • Prunes lattice structure of non-frequent itemsets using minsup. • Reduces the number of comparisons • Reduces the number of candidate itemsets

  17. Monotonicity Monotone (upward closed) - if X is a subset of Y, then support(X) cannot exceed support(Y). Anti-Monotone (downward closed) - if X is a subset of Y, then support(Y) cannot exceed support(X). Apriori is anti-monotone. - uses this property to prune the lattice structure.

  18. Itemset Lattice

  19. Lattice Pruning

  20. Lattice Example Count occurrences of each 1-itemset in the database and compute their support: Support = #occurrences/#rows in db Prune anything less than minsup = 30%

  21. Lattice Example Count occurrences of each 2-itemset in the database and compute their support Prune anything less than minsup = 30%

  22. Lattice Example Count occurrences of the last 3-itemset in the database and compute its support. Prune anything less than minsup = 30%

  23. Example - Results Frequent itemsets: {1}, {2}, {3}, {1,2}, {1,3}, {2,3}, {1,2,3}

  24. Apriori Algorithm

  25. Frequent Itemset Generation • Minsup = 70% • Generate all 1-itemsets • Calculate the support for each itemset • Determine whether or not the itemsets are frequent

  26. Frequent Itemset Generation Generate all 2-itemsets, minsup = 70% {1} U {3} = {1,3} , {1} U {5} = {1,5} {3} U {5} = {3,5}

  27. Frequent Itemset Generation Generate all 3-itemsets, minsup = 70% {1,3} U {1,5} = {1,3,5}

  28. Frequent Itemset Results All frequent itemsets generated are output: {1} , {3} , {5} {1,3} , {1,5} , {3,5} {1,3,5}

  29. Apriori Rule Mining

  30. Apriori Rule Mining Rule Combinations: 1. {1,2} 2-itemsets {1}=>{2} {2}=>{1} 2. {1,2,3} 3-itemsets {1}=>{2,3} {2,3}=>{1} {1,2}=>{3} {3}=>{1,2} {1,3}=>{2} {2}=>{1,3}

  31. Strong Rule Generation • I = {{1}, {3}, {5}} • Rules = X => Y • Minconf = 80%

  32. Strong Rule Generation • I = {{1}, {3}, {5}} • Rules = X => Y • Minconf = 80%

  33. Strong Rules Results All strong rules generated are output: {1}=>{5} {3}=>{5} {2}=>{3,5} {2,3}=>{5} {2,5}=>{3}

  34. Other Frequent Itemsets Closed Frequent Itemset – a frequent itemset X who has no immediate supersets with the same support count as X. Maximal Frequent Itemset – a frequent itemset whom none of its immediate supersets are frequent.

  35. Itemset Relationships Frequent Itemsets Closed Frequent Itemsets Maximal Frequent Itemsets

  36. Targeted Association Mining

  37. Targeted Association Mining * Users may only be interested in specific results * Potential to get smaller, faster, and more focused results * Examples: 1. User wants to know how often only bread and garlic cloves occur together. 2. User wants to know what items occur with toilet paper.

  38. Itemset Trees * Itemset Tree: - A data structure which aids in users querying for a specific itemset and it’s support. * Items within a transaction are mapped to integer values and ordered such that each transaction is in lexical order. {Bread, Onion, Garlic} = {1, 2, 3} * Why use numbers? - make the tree more compact - numbers follow ordering easily

  39. Itemset Trees An Itemset Tree T contains: * A root pair (I, f(I)), where I is an itemset and f(I) is its count. * A (possibly empty) set {T1, T2, . . . , Tk} each element of which is an itemset tree. * If Ij is in the root, then it will also be in The root’s children * If Ij is not in the root, then it might be in the root’s children if: first_item(I) < first_item(Ij) and last_item(I) < last_item(Ij)

  40. Building an Itemset Tree Let ci be a node in the itemset tree. Let I be a transaction from the dataset Loop: Case 1: ci = I Case 2: ci is a child of I - make I the parent node of ci Case 3: ci and I contain a common lexical overlap i.e. {1,2,4} vs. {1,2,6} - make a node for the overlap - make I and ci it’s children. Case 4: ci is a parent of I - Loop to check ci’s children - make I a child of ci Note: {2,6} and {1,2,6} do not have a Lexical overlap

  41. Itemset Trees - Creation

  42. Itemset Trees - Creation Child node.

  43. Itemset Trees - Creation Child node.

  44. Itemset Trees - Creation Child node.

  45. Itemset Trees - Creation Lexical overlap

  46. Itemset Trees - Creation Parent node.

  47. Itemset Trees - Creation Child node.

  48. Itemset Trees – Querying Let I be an itemset, Let cibe a node in the tree Let totalSup be the total count for I in the tree For all s.t. first_item(ci) < first_item(I): Case 1: If I is contained in ci. - Add support to totalSup. Case 2: If I is not contained and last_item(ci) < last_item(I) - proceed down the tree

  49. Example 1

  50. Itemset Trees - Querying • Querying Example 1: • Query: {2} • totalSup = 0

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