Associations and Frequent Item Analysis

Transcription

1 CS 1655 / Spring 2010 Secure Data Management and Web Applications 02 Data Mining (cont) Alexandros Labrinidis University of Pittsburgh Associations and Frequent Item Analysis 1

2 Outline Transactions Frequent itemsets Subset Property Association rules Applications 3 Transactions Example TID Produce 1 MILK, BREAD, EGGS 2 BREAD, SUGAR 3 BREAD, CEREAL 4 MILK, BREAD, SUGAR 5 MILK, CEREAL 6 BREAD, CEREAL 7 MILK, CEREAL 8 MILK, BREAD, CEREAL, EGGS 9 MILK, BREAD, CEREAL 4 2

3 Transaction database: Example TID Products 1 A, B, E 2 B, D 3 B, C 4 A, B, D 5 A, C 6 B, C 7 A, C 8 A, B, C, E 9 A, B, C ITEMS: A = milk B = bread C = cereal D = sugar E = eggs Instances = Transactions 5 Transaction database: Example Attributes converted to binary flags TID Products 1 A, B, E 2 B, D 3 B, C 4 A, B, D 5 A, C 6 B, C 7 A, C 8 A, B, C, E 9 A, B, C TID A B C D E

4 Definitions Item: attribute=value pair or simply value usually attributes are converted to binary flags for each value, e.g. product= A is written as A Itemset I : a subset of possible items Example: I = {A,B,E} (order unimportant) Transaction: (TID, itemset) TID is transaction ID 7 Support and Frequent Itemsets Support of an itemset sup(i ) = no. of transactions t that support (i.e. contain) I In example database: sup ({A,B,E}) = 2, sup ({B,C}) = 4 Frequent itemset I is one with at least the minimum support count sup(i ) >= minsup 8 4

5 SUBSET PROPERTY 9 Association Rules Association rule R : Itemset1 => Itemset2 Itemset1, 2 are disjoint and Itemset2 is non-empty meaning: if transaction includes Itemset1 then it also has Itemset2 Examples A,B => E,C A => B,C 10 5

6 From Frequent Itemsets to Association Rules Q: Given frequent set {A,B,E}, what are possible association rules? A => B, E A, B => E A, E => B B => A, E B, E => A E => A, B => A,B,E (empty rule), or true => A,B,E 11 Classification vs Association Rules Classification Rules Focus on one target field Specify class in all cases Measures: Accuracy Association Rules Many target fields Applicable in some cases Measures: Support, Confidence, Lift 12 6

7 Definition of Support for Rules Association Rule R: I => J Example: {A, B} => {C} Support for R: sup(r) = sup (I => J) = sup(i U J) Example: sup({a,b}=>{c}) = sup ({A,B} U {C} = sup ({A,B,C}) = 2/9 Meaning: fraction of transactions that involve both left-hand side (LHS) and right-hand side (RHS) itemsets 13 Definition of Confidence for Association Rules Association Rule R: I => J Example: {A, B} => {C} Confidence for R: conf(r) = conf(i=>j) = sup(i U J) / sup( I ) Example: conf({a,b}=>{c}) = sup ({A,B,C}) / sup({a,b}) = = (2/9) / (4/9) = 50% Meaning: probability that RHS will appear given that LHS appears 14 7

8 Association Rules Example: Q: Given frequent set {A,B,E}, what association rules have minsup = 2 and minconf= 50%? A, B => E : conf=2/4 = 50% A, E => B : conf=2/2 = 100% B, E => A : conf=2/2 = 100% E => A, B : conf=2/2 = 100% Don t qualify A =>B, E : conf=2/6 =33%< 50% B => A, E : conf=2/7 = 28% < 50% => A,B,E : conf: 2/9 = 22% < 50% TID List of items 1 A, B, E 2 B, D 3 B, C 4 A, B, D 5 A, C 6 B, C 7 A, C 8 A, B, C, E 9 A, B, C 15 Find Strong Association Rules A rule has the parameters minsup and minconf: sup(r) >= minsup and conf (R) >= minconf Problem: Find all association rules with given minsup and minconf First, find all frequent itemsets 16 8

9 Finding Frequent Itemsets Start by finding one-item sets (easy) Q: How? A: Simply count the frequencies of all items 17 Finding itemsets: next level Apriori algorithm (Agrawal & Srikant) Idea: use one-item sets to generate two-item sets, two-item sets to generate three-item sets, If (A B) is a frequent item set, then (A) and (B) have to be frequent item sets as well! In general: if X is frequent k-item set, then all (k-1)- item subsets of X are also frequent Compute k-item set by merging (k-1)-item sets 18 9

10 An example Given: five three-item sets!(a B C), (A B D), (A C D), (A C E), (B C D)! Lexicographic order improves efficiency Candidate four-item sets: (A B C D)! Q: OK?! A: yes, because all 3-item subsets are frequent!(a C D E) Q: OK?! A: No, because (C D E) is not frequent 19 Beyond Binary Data Hierarchies drink milk low-fat milk Stop&Shop low-fat milk find associations on any level Sequences over time 20 10

11 Applications Market basket analysis Store layout, client offers Finding unusual events WSARE What is Strange About Recent Events 21 Application Difficulties Wal-Mart knows that customers who buy Barbie dolls have a 60% likelihood of buying one of three types of candy bars. What does Wal-Mart do with information like that? 'I don't have a clue,' says Wal-Mart's chief of merchandising, Lee Scott See - KDnuggets 98:01 for many ideas Diapers and beer urban legend 22 11

12 Summary Frequent itemsets Association rules Subset property Apriori algorithm Application difficulties 23 Clustering 12

13 Outline Introduction K-means clustering Hierarchical clustering: COBWEB 25 Classification vs. Clustering Classification: Supervised learning: Learns a method for predicting the instance class from pre-labeled (classified) instances 26 13

14 Clustering Unsupervised learning: Finds natural grouping of instances given un-labeled data 27 Clustering Methods Many different method and algorithms: For numeric and/or symbolic data Deterministic vs. probabilistic Exclusive vs. overlapping Hierarchical vs. flat Top-down vs. bottom-up 28 14

15 Clusters: exclusive vs. overlapping Simple 2-D representation Non-overlapping Venn diagram Overlapping a d k g j h e i f c b a d k g j h e i f c b 29 Clustering Evaluation Manual inspection Benchmarking on existing labels Cluster quality measures distance measures high similarity within a cluster, low across clusters 30 15

16 The distance function Simplest case: one numeric attribute A Distance(X,Y) = A(X) A(Y) Several numeric attributes: Distance(X,Y) = Euclidean distance between X,Y Nominal attributes: distance is set to 1 if values are different, 0 if they are equal Are all attributes equally important? Weighting the attributes might be necessary 31 Simple Clustering: K-means Works with numeric data only 1) Pick a number (K) of cluster centers (at random) 2) Assign every item to its nearest cluster center (e.g. using Euclidean distance) 3) Move each cluster center to the mean of its assigned items 4) Repeat steps 2,3 until convergence (change in cluster assignments less than a threshold) 32 16

17 K-means example, step 1 Y k 1 Pick 3 initial cluster centers (randomly) k 2 k 3 X 33 K-means example, step 2 Y k 1 Assign each point to the closest cluster center k 2 k 3 X 34 17

18 K-means example, step 3 Y Move each cluster center to the mean of each cluster k 2 k 2 k 1 k 1 k 3 k 3 X 35 K-means example, step 4 Reassign points closest to a different new cluster center Y Q: Which points are reassigned? k 1 k 2 k 3 X 36 18

19 K-means example, step 4 Y k 1 A: three points with animation k 2 k 3 X 37 K-means example, step 4b Y k 1 re-compute cluster means k 2 k 3 X 38 19

20 K-means example, step 5 Y k 1 move cluster centers to cluster means k 2 k 3 X 39 Discussion Result can vary significantly depending on initial choice of seeds Can get trapped in local minimum Example: initial cluster centers instances To increase chance of finding global optimum: restart with different random seeds 40 20

21 K-means clustering summary Advantages Simple, understandable items automatically assigned to clusters Disadvantages Must pick number of clusters before hand All items forced into a cluster Too sensitive to outliers 41 K-means variations K-medoids instead of mean, use medians of each cluster 5 Mean of 1, 3, 5, 7, 9 is Mean of 1, 3, 5, 7, 1009 is 205 Median of 1, 3, 5, 7, 1009 is 5 Median advantage: not affected by extreme values For large databases, use sampling 42 21

22 *Hierarchical clustering Bottom up Start with single-instance clusters At each step, join the two closest clusters Design decision: distance between clusters E.g. two closest instances in clusters vs. distance between means Top down Start with one universal cluster Find two clusters Proceed recursively on each subset Can be very fast Both methods produce a dendrogram g a c i e d k b j f h 43 Discussion Can interpret clusters by using supervised learning learn a classifier based on clusters Decrease dependence between attributes? pre-processing step E.g. use principal component analysis Can be used to fill in missing values Key advantage of probabilistic clustering: Can estimate likelihood of data Use it to compare different models objectively 44 22

23 Examples of Clustering Applications Marketing: discover customer groups and use them for targeted marketing and re-organization Astronomy: find groups of similar stars and galaxies Earth-quake studies: Observed earth quake epicenters should be clustered along continent faults Genomics: finding groups of gene with similar expressions 45 Clustering Summary unsupervised many approaches K-means simple, sometimes useful K-medoids is less sensitive to outliers Hierarchical clustering works for symbolic attributes Evaluation is a problem (i.e., quality control is hard) 46 23