#### Transcript Association Rule Mining

Data Mining and Knowledge Acquisition — Chapter 6 — BIS 541 2013/2014 Summer 1 Chapter 5: Mining Association Rules in Large Databases Association rule mining Mining single-dimensional Boolean association rules from transactional databases Mining multilevel association rules from transactional databases Mining multidimensional association rules from transactional databases and data warehouse From association mining to correlation analysis Constraint-based association mining Summary 2 What Is Association Mining? Association rule mining: Finding frequent patterns, associations, correlations, or causal structures among sets of items or objects in transaction databases, relational databases, and other information repositories. Applications: Market basket analysis, cross-marketing, catalog design, etc. Examples. Rule form: “Body ead [support, confidence]”. buys(x, “diapers”) buys(x, “beers”) [0.5%, 60%] major(x, “MIS”) ^ takes(x, “DM”) grade(x, “AA”) [1%, 75%] 3 Association Rule: Basic Concepts Given: (1) database of transactions, (2) each transaction is a list of items (purchased by a customer in a visit) Find: all rules that correlate the presence of one set of items with that of another set of items E.g., 98% of people who purchase tires and auto accessories also get automotive services done The user specifies Minimum support level Minimum confidence level Rules exceeding the two trasholds are listed as interesting 4 Basic Concepts cont. I:{i1,..,im} set of all items, T any transaction AT: T contains the itemset A AT, BT A,B itemsets Examine rule like: AB where AB=, support s: P(AB) frequency of transactions containing both A and B confidence c: P(BA) = P(AB)/P(A) Conditional probability that a transaction containing A contains B 5 Rule Measures: Support and Confidence Customer buys both Find all the rules X & Y Z with minimum confidence and support support, s, probability that a transaction contains {X Y Z} confidence, c, conditional Customer buys beer probability that a transaction having {X Y} also contains Z Transaction ID Items Bought Let minimum support 50%, and minimum confidence 50%, 2000 A,B,C we have 1000 A,C A C (50%, 66.6%) 4000 A,D 5000 B,E,F C A (50%, 100%) Customer buys diaper 6 Frequent itemsets Strong association rules: Support rule > min_support Confidence rule > min_confidence k-item set: itemsets containing k items occurrence frequency=count=support count: Minimum support count = min_sup*#transactions in database frequent item sets: İtemsets satisfying minimum support count The Apriori Algorithm has two steps: (1) - Find all frequent itemsets (2) - Genertate strong association rules from frequent itemsets 7 Mining Association Rules—An Example(1) Transaction ID 2000 1000 4000 5000 Items Bought A,B,C A,C A,D B,E,F Min_support 50% Min._confidence 50% Min_count:0.5*4=2 Frequent Itemset Support {A} 75% {B} 50% {C} 50% {D} 25% {A}.{B}.{C}.{D} are 1-itemsets {A}.{B}.{C} are frequent 1-itemsets as Count[{A}] = 3 >= 2 (minimum_count) or Support[{A}] = 75% >= 50% (minimum_support) {D} is not a frequent 1-itemsets as Count[{D}] = 1 < 2 (minimum_count) or Support[{D}] = 25% < 50% (minimum_support) 8 Mining Association Rules—An Example(2) Transaction ID 2000 1000 4000 5000 Items Bought A,B,C A,C A,D B,E,F Min_support 50% Min._confidence 50% Min_count:0.5*4=2 Frequent Itemset Support {A.B} 25% {A.C} 50% {A.D} 25% {B,C} 25% {A.B}.{A.C}.{A.D}.{B.C} are 2-itemsets {A.C}is frequent 2-itemsets as Count[{A.C}] = 2 >= 2 (minimum_count) or Support[{A.C}] = 50% >= 50% (minimum_support) {A.B}.{A.D} are not frequent 2-itemsets as Count[{A.D}] = 1 < 2 (minimum_count) or Support[{A.D}] = 25% < 50% (minimum_support) 9 Mining Association Rules—An Example(3) Transaction ID 2000 1000 4000 5000 Items Bought A,B,C A,C A,D B,E,F Min. support 50% Min. confidence 50% Frequent Itemset Support {A} 75% {B} 50% {C} 50% {A,C} 50% For rule A C: support = support({A C}) = 50% confidence = support({A C})/support({A}) = 66.6% Strong rule as support >=min_support confidence >= min_confidence 10 The Apriori Principle Transaction ID 2000 1000 4000 5000 Items Bought A,B,C A,C A,D B,E,F Min. support 50% Min. confidence 50% Frequent Itemset Support {A} 75% {B} 50% {C} 50% {A,C} 50% The Apriori principle: Any subset of a frequent itemset must be frequent {A.C} is a frequent 2-itemset {A} and {C}: subsets of {A,C} must be frequent 1-itemsets 11 Apriori Algorithme has two steps (1)-Find the frequent itemsets: the sets of items that have minimum support (the key step) A subset of a frequent itemset must also be a frequent itemset Iteratively find frequent itemsets with cardinality from 1 to k (kitemset) i.e., if {AB} is a frequent itemset, both {A} and {B} should be a frequent itemsets Until k is an empty set (2)-Use the frequent itemsets to generate association rules. 12 Generation of frequent itemsets from candidate itemsets (Step 1) C1L1 C2L2 C3 L3 C4L4… From Ck (candidate k-itemsets) generate Lk :Ck Lk From candidate k itemsets generate frequent k itemsets (a)-Using the Apriori principle that: Eliminate itemset sk in Ck if (b)-For candidate k itemsets in Ck At least one k-1 subset of sk is not in Lk-1 Make a database scan to eliminate those itemsets whose support counts are below the critical min support cout From frequent k itemsets Lk generate candidate k+1 itemsets Ck+1 : Lk Ck+1 Self joining any Lk with Lk 13 Self Join operation Sort the items in any li Lk in some lexicographic order li[1]<li[2]<,… <li[k-1]<li[k] li and lj are elements of Lk li.lj Lk If li[1]=lj[1] and li[2]=lj[2] and … li[k-1]=lj[k-1] and li[k]<lj[k] The first k-1 elements are the same Only the last elements are different li lj satisfiing the above condition Construct the item set lk+1: li[1], li[2],… li[k-1],li[k], lj[k] common items the k-1 items are taken form li or lj k th item is taken from li k+1 th item is from lj 14 Example of Self Join operation Lexigographic order: alphabetic a<b<c<d.... L3={abc, abd, acd, ace, bcd} Self-joining: L3*L3 Step(2) abcd from abc and abd acde from acd and ace Pruning by Apriori principle: Step(1a) acde is removed because ade is not in L3 C4={abcd} 15 The Apriori Algorithm — Example min support cont=2 Database D TID 100 200 300 400 itemset sup. C1 {1} 2 {2} 3 Scan D {3} 3 {4} 1 {5} 3 Items 134 235 1235 25 C2 itemset sup L2 itemset sup 2 2 3 2 {1 {1 {1 {2 {2 {3 C3 itemset {2 3 5} Scan D {1 3} {2 3} {2 5} {3 5} 2} 3} 5} 3} 5} 5} 1 2 1 2 3 2 L1 itemset sup. {1} {2} {3} {5} 2 3 3 3 C2 itemset {1 2} Scan D {1 {1 {2 {2 {3 3} 5} 3} 5} 5} L3 itemset sup {2 3 5} 2 16 Example 6.1 Han TID_____list of item_Ids T100 125 9 transactions T200 24 D=9 T300 23 minimum transaction T400 124 support_count=2 T500 13 min_sup=2/9=22% T600 23 T700 13 min conf: 70% T800 1235 T900 123 Find strong association rules: having min sup count of 2 and min confidence %70 17 Data Dictionary 1: 2: 3: 4: 5: milk apple butter bread orange 18 1th iteration of algorithm C1: itemset sup_count L1:itemset 1 6 1 2 7 2 3 6 3 4 2 4 5 2 5 sup_count 6 7 6 2 2 C2:L1 join L1, itemset sup_count 1 1 1 1 2 2 2 3 3 4 2 3 4 5 3 4 5 4 5 5 4 4 1x 2 4 2 2 0x 1x 0x L2 itset supcount 12 4 13 4 15 2 23 4 24 2 25 2 frequent 2 item sets L2 those itemsets in C2 having minimum support Step (1b) 19 3 th iteration Self join to get C3 Step (2) C3: L2 join L2: [1 2 3], [1 2 5],[1 3 5],[2 3 4], [2 3 5],[2 4 5] Now Step (1a) Apply Apriori to every itemset in C3 2 item subsets of [1 2 3]:[1 2],[1 3],[2 3] all 2 items sets are members of L2 keep [1 2 3] in C3 2 item subsets of [1 2 5]:[1 2],[1 5],[2 5] all 2 items sets are members of L2 keep [1 2 5] in C3 2 item subsets of [1 3 5]:[1 3],[1 5],[3 5] [3 5] is not a members of L2 so it si not frequent remove [1 2 5] from C3 20 3 iteration cont. 2 item subsets of [2 3 4]:[2 3],[2 4],[3 4] [3 4] is not a members of L2 so it si not frequent remove [2 3 4] from C3 2 item subsets of [2 3 5]:[2 3],[2 5],[3 5] [3 5] is not a members of L2 so it si not frequent remove [2 3 5] from C3 2 item subsets of [2 4 5]:[2 4],[2 5],[4 5] [4 5] is not a members of L2 so it si not frequent remove [2 4 5] from C3 C3:[1 2 3],[1 2 5] after pruning 21 4 th iteration C3L3 check min support Step (1b) L3:those item sets having minimum support L3: item sets minsupcount 1 2 3 2 1 2 5 2 L3 join L3 to generate C4 Step (2) L3 join L3: 1 2 3 5 pruned since its subset [2 3 5] is not frequent C4= the algorithm terminates 22 Generating Association Rules from frequent itemsets Strong rules min support and min confidence confidence(AB)= P(BA):sup_count(AB) sup_count(A) for each frequent itemset l generate non empty subsets of l: denoted by s For each sl construct rules: s (l-s) Satısfying the condition: sup_count(l)/sup_count(s)>=min_conf are listed as interestıng 23 Example 6.2 Han cont. the 3-frequent item set l:[1 2 5]: transaction containing milk, apple and orange is frequent non empty subsets of l are [1 2],[1 5],[2 5],[1],[2],[5] the resulting association rules are: 125 conf: 2/4=50% 152 conf: 2/2=100% 251 conf: 2/2=100% 125 conf: 2/6=33% 215 conf: 2/7=29% 512 conf: 2/2=100% if min conf: 70% 2th 3th and last rules are strong 24 Example 6.2 cont. Detail on confidence for two rules For the rule 152 conf: s(1,2,5)/s(1,5) conf: 2/2=100% >= 70% A strong rule For the rule 215 conf: s(1,2,5)/s(2) conf: 2/7=29% < 70% Not a strong rule 25 Exercise Find all strong association rules in Example 6.2 Check minimum confindence for 2-frequent intemsets [1,2], [1,3], [1,5], [2,3], [2,4], [2,5] 12, 21 25, 52 exetra for 3-frequent intemset [1,2,5] 123 3 12 exetra 26 Exercise a) Suppose A B and B C are strong rules Dose this imply that A C is also a strong rule? b) Suppose A B and A C are strong rules Dose this imply that B C is also a strong rule? c) Suppose A C and B C are strong rules Dose this imply that A and B C is also a strong rule? 27 Bottleneck of Frequent-pattern Mining Multiple database scans are costly Mining long patterns needs many passes of scanning and generates lots of candidates To find frequent itemset i1i2…i100 # of scans: 100 # of Candidates: (1001) + (1002) + … + (110000) = 21001 = 1.27*1030 ! Bottleneck: candidate-generation-and-test Can we avoid candidate generation? 28 Is Apriori Fast Enough? — Performance Bottlenecks The core of the Apriori algorithm: Use frequent (k – 1)-itemsets to generate candidate frequent kitemsets Use database scan and pattern matching to collect counts for the candidate itemsets The bottleneck of Apriori: candidate generation Huge candidate sets: 104 frequent 1-itemset will generate 107 candidate 2-itemsets To discover a frequent pattern of size 100, e.g., {a1, a2, …, a100}, one needs to generate 2100 1030 candidates. Multiple scans of database: Needs (n +1 ) scans, n is the length of the longest pattern 29 Mining Frequent Patterns Without Candidate Generation Compress a large database into a compact, FrequentPattern tree (FP-tree) structure highly condensed, but complete for frequent pattern mining avoid costly database scans Develop an efficient, FP-tree-based frequent pattern mining method A divide-and-conquer methodology: decompose mining tasks into smaller ones Avoid candidate generation: sub-database test only! 30 Construct FP-tree from a Transaction DB TID 100 200 300 400 500 Items bought (ordered) frequent items {f, a, c, d, g, i, m, p} {f, c, a, m, p} {a, b, c, f, l, m, o} {f, c, a, b, m} {b, f, h, j, o} {f, b} {b, c, k, s, p} {c, b, p} {a, f, c, e, l, p, m, n} {f, c, a, m, p} Steps: 1. Scan DB once, find frequent 1-itemset (single item pattern) 2. Order frequent items in frequency descending order 3. Scan DB again, construct FP-tree min_support = 0.5 {} Header Table Item frequency head f 4 c 4 a 3 b 3 m 3 p 3 f:4 c:3 c:1 b:1 a:3 b:1 p:1 m:2 b:1 p:2 m:1 31 Benefits of the FP-tree Structure Completeness: never breaks a long pattern of any transaction preserves complete information for frequent pattern mining Compactness reduce irrelevant information—infrequent items are gone frequency descending ordering: more frequent items are more likely to be shared never be larger than the original database (if not count node-links and counts) Example: For Connect-4 DB, compression ratio could be over 100 32 Chapter 5: Mining Association Rules in Large Databases Association rule mining Mining single-dimensional Boolean association rules from transactional databases Mining multilevel association rules from transactional databases Mining multidimensional association rules from transactional databases and data warehouse From association mining to correlation analysis Constraint-based association mining Summary 33 Multiple-Level Association Rules Food Items often form hierarchy. Items at the lower level are expected to have lower support. Rules regarding itemsets at appropriate levels could be quite useful. Transaction database can be encoded based on dimensions and levels We can explore shared multilevel mining bread milk skim Fraser TID T1 T2 T3 T4 T5 2% wheat white Sunset Items {111, 121, 211, 221} {111, 211, 222, 323} {112, 122, 221, 411} {111, 121} {111, 122, 211, 221, 413} 34 Mining Multi-Level Associations A top_down, progressive deepening approach: First find high-level strong rules: milk bread [20%, 60%]. Then find their lower-level “weaker” rules: 2% milk wheat bread [6%, 50%]. Variations at mining multiple-level association rules. Level-crossed association rules: 2% milk Wonder wheat bread Association rules with multiple, alternative hierarchies: 2% milk Wonder bread 35 Multi-level Association: Uniform Support vs. Reduced Support Uniform Support: the same minimum support for all levels + One minimum support threshold. No need to examine itemsets containing any item whose ancestors do not have minimum support. – Lower level items do not occur as frequently. If support threshold too high miss low level associations too low generate too many high level associations Reduced Support: reduced minimum support at lower levels There are 4 search strategies: Level-by-level independent Level-cross filtering by k-itemset Level-cross filtering by single item Controlled level-cross filtering by single item 36 Uniform Support Multi-level mining with uniform support Level 1 min_sup = 5% Level 2 min_sup = 5% Milk [support = 10%] 2% Milk Skim Milk [support = 6%] [support = 4%] Back 37 Reduced Support Multi-level mining with reduced support Level 1 min_sup = 5% Level 2 min_sup = 3% Milk [support = 10%] 2% Milk Skim Milk [support = 6%] [support = 4%] Back 38 Controlled level-cross filtering by single item Specify a level passage treshold for each level k min_sup_T(k+1)<LPT(k)<min_sup_T(k) Example: High level milk Low level 2% milk,skim milk min supp=5% Min supp = 3% Level passage trashold = 4% 39 Multi-level Association: Redundancy Filtering Some rules may be redundant due to “ancestor” relationships between items. Example milk wheat bread [support = 8%, confidence = 70%] 2% milk wheat bread [support = 2%, confidence = 72%] We say the first rule is an ancestor of the second rule. A rule is redundant if its support is close to the “expected” value, based on the rule’s ancestor. 40 Multi-Level Mining: Progressive Deepening A top-down, progressive deepening approach: First mine high-level frequent items: milk (15%), bread (10%) Then mine their lower-level “weaker” frequent itemsets: 2% milk (5%), wheat bread (4%) Different min_support threshold across multi-levels lead to different algorithms: If adopting the same min_support across multilevels then toss t if any of t’s ancestors is infrequent. If adopting reduced min_support at lower levels then examine only those descendents whose ancestor’s support is frequent/non-negligible. 41 Progressive Refinement of Data Mining Quality Why progressive refinement? Trade speed with quality: step-by-step refinement. Superset coverage property: Mining operator can be expensive or cheap, fine or rough Preserve all the positive answers—allow a positive false test but not a false negative test. Two- or multi-step mining: First apply rough/cheap operator (superset coverage) Then apply expensive algorithm on a substantially reduced candidate set (Koperski & Han, SSD’95). 42 Chapter 5: Mining Association Rules in Large Databases Association rule mining Mining single-dimensional Boolean association rules from transactional databases Mining multilevel association rules from transactional databases Mining multidimensional association rules from transactional databases and data warehouse From association mining to correlation analysis Constraint-based association mining Summary 43 Interestingness Measurements Objective measures Two popular measurements: support; and confidence Subjective measures (Silberschatz & Tuzhilin, KDD95) A rule (pattern) is interesting if it is unexpected (surprising to the user); and/or actionable (the user can do something with it) 44 Criticism to Support and Confidence Example 1: (Aggarwal & Yu, PODS98) Among 5000 students 3000 play basketball 3750 eat cereal 2000 both play basket ball and eat cereal play basketball eat cereal [40%, 66.7%] is misleading because the overall percentage of students eating cereal is 75% which is higher than 66.7%. play basketball not eat cereal [20%, 33.3%] is far more accurate, although with lower support and confidence basketball not basketball sum(row) cereal 2000 1750 3750 not cereal 1000 250 1250 sum(col.) 3000 2000 5000 45 Criticism to Support and Confidence (Cont.) Example 2: X and Y: positively correlated, X and Z, negatively related support and confidence of X=>Z dominates We need a measure of dependent or correlated events corrA, B P( A B) P( A) P( B) X 1 1 1 1 0 0 0 0 Y 1 1 0 0 0 0 0 0 Z 0 1 1 1 1 1 1 1 Rule Support Confidence X=>Y 25% 50% X=>Z 37.50% 75% P(B|A)/P(B) is also called the lift of rule A => B 46 Other Interestingness Measures: Interest Interest (correlation, lift) P( A B) P( A) P( B) taking both P(A) and P(B) in consideration P(A^B)=P(B)*P(A), if A and B are independent events A and B negatively correlated, if the value is less than 1; otherwise A and B positively correlated X 1 1 1 1 0 0 0 0 Y 1 1 0 0 0 0 0 0 Z 0 1 1 1 1 1 1 1 Itemset Support Interest X,Y X,Z Y,Z 25% 37.50% 12.50% 2 0.9 0.57 47 Example Total transactions 10,000 İtems C:computers, V: video V: 7,500 C: 6,000 C and V: 4,000 Min_support: 0.3 min_conf:0,50 Consider the rule: Buy(X: computer) buy(X: video) Support : = 4000/10000 = 0.4 Confidence: P(C and V) /P(C) = 4000/6000 =%66 Strong but The probablity of buying a video is 0.75 buying a comuter reduces the probablity of buying a video From 0.75 to 0.66 Computer and video are negatively correlated 48 Lift of A B Lift : P(A and B)/P(A)*P(B) P(A and B) = P(B|A)*P(A) then Lift = P(B|A)/P(B) Ratio of probablity of buying A and B divided by buying A and B independently Or it can be interpreted as: Conditional probablity of buying B given that A is purchased divided by unconditional probablity of buying B 49 C not C V 4000 3500 not V 2000 500 7500 2500 10000 4000 6000 Lift CV is P(P and V)/P(V)P(C) = P(V|C)/P(V) = 0.4/0.6*0.75=0.89<1 there is a negative correlation Between Video and computer 50 Are All the Rules Found Interesting? “Buy walnuts buy milk [1%, 80%]” is misleading if 85% of customers buy milk Support and confidence are not good to represent correlations So many interestingness measures? (Tan, Kumar, Sritastava @KDD’02) lift P( A B) P( A) P( B) all _ conf sup( X ) max_item _ sup( X ) sup( X ) coh | universe( X ) | Milk No Milk Sum (row) Coffee m, c ~m, c c No Coffee m, ~c ~m, c ~c Sum(col.) m ~m all-conf coh 2 9.26 0.91 0.83 9055 100,000 8.44 0.09 0.05 670 10000 100,000 9.18 0.09 0.09 8172 1000 1000 1 0.5 0.33 0 DB m, c ~m, c m~c ~m~c lift A1 1000 100 100 10,000 A2 100 1000 1000 A3 1000 100 A4 1000 1000 51 All Confidence All confidence: All_conf= sup(X)/max sup(Xi)i X: (X1,X2,...,Xk) For k = 2 Rules are X1X2 and X2 X1 All_conf = sup(X1,X2)/max sup(X1),sup(X2) Here sup(X1,X2)/sup(X1): confidence of rule X1X2 Ex all conf: 0.4/max(0.6,0.75)=0.4/0.75=0.53 52 Cosine Cosine : P(A,B)/sqrt(P(A),P(B)) Similar to lift but take square root of denominator Both cosine and all_conf are null inveriant Not affected from null transactions Ex: Cosine: 0.4/sqrt(0.6*0.75)=0.27 53 Mining Highly Correlated Patterns lift and 2 are not good measures for correlations in transactional DBs all-conf or cosine could be good measures (Omiecinski @TKDE’03) Both all-conf and coherence have the downward closure all _ conf sup( X ) DB max_item _ sup( X ) m, c ~m, c m~c ~m~c lift A1 1000 100 100 10,000 A2 100 1000 1000 A3 1000 100 A4 1000 1000 sup( X ) coh | universe( X ) | all-conf coh 2 9.26 0.91 0.83 9055 100,000 8.44 0.09 0.05 670 10000 100,000 9.18 0.09 0.09 8172 1000 1000 1 0.5 0.33 0 54 Dataset mc mc mc mc all_conf. cosine 2 lift A1 1000 100 100 100000 0.91 0.91 83.64 83452.6 A2 1000 100 100 10000 0.91 0.91 9.36 9055.7 A3 1000 100 100 1000 0.91 0.91 1.82 1472.7 A4 1000 100 100 0 0.91 0.91 0.99 9.9 B1 1000 1000 1000 1000 0.5 0.5 1 0 C1 100 1000 1000 100000 0.09 0.09 8.44 670 C2 1000 100 10000 100000 0.09 0.29 9.18 8172.8 C3 1 1 100 10000 0.1 0.07 50 48.5 55 Chapter 5: Mining Association Rules in Large Databases Association rule mining Mining single-dimensional Boolean association rules from transactional databases Mining multilevel association rules from transactional databases Mining multidimensional association rules from transactional databases and data warehouse From association mining to correlation analysis Constraint-based association mining Summary 56 Constraint-based (Query-Directed) Mining Finding all the patterns in a database autonomously? — unrealistic! The patterns could be too many but not focused! Data mining should be an interactive process User directs what to be mined using a data mining query language (or a graphical user interface) Constraint-based mining User flexibility: provides constraints on what to be mined System optimization: explores such constraints for efficient mining—constraint-based mining 57 Constraints in Data Mining Knowledge type constraint: classification, association, etc. Data constraint — using SQL-like queries find product pairs sold together in stores in Chicago in Dec.’02 Dimension/level constraint in relevance to region, price, brand, customer category Rule (or pattern) constraint small sales (price < $10) triggers big sales (sum > $200) Interestingness constraint strong rules: min_support 3%, min_confidence 60% 58 Example bread milk milk butter Strong rules but items are not that valuable TV VCD player Support may be lower then previous rules but value of items are much higher This rule may be more valuable 59 Apriori principle stating that All non empty subsets of a frequent itemsets must also be frequent Note that: If a given itemset does not satisfy minimum support None of its supersets can Other examples of anti-monotone constraints: Min(l.price) >= 500 Count(l) < 10 Average(l.price) < 10 : not anti-monotone 60 Anti-Monotonicity in Constraint Pushing TDB (min_sup=2) Anti-monotonicity When an intemset S violates the constraint, so does any of its superset sum(S.Price) v is anti-monotone sum(S.Price) v is not anti-monotone Example. C: range(S.profit) 15 is antimonotone Itemset ab violates C So does every superset of ab TID Transaction 10 a, b, c, d, f 20 b, c, d, f, g, h 30 a, c, d, e, f 40 c, e, f, g Item Profit a 40 b 0 c -20 d 10 e -30 f 30 g 20 h -10 61 Monotonicity for Constraint Pushing TDB (min_sup=2) Monotonicity sum(S.Price) v is monotone Transaction 10 a, b, c, d, f 20 b, c, d, f, g, h 30 a, c, d, e, f 40 c, e, f, g Item Profit min(S.Price) v is monotone a 40 b 0 Example. C: range(S.profit) 15 c -20 d 10 e -30 f 30 g 20 h -10 When an intemset S satisfies the constraint, so does any of its superset TID Itemset ab satisfies C So does every superset of ab 62 The Apriori Algorithm — Example Database D TID 100 200 300 400 itemset sup. C1 {1} 2 {2} 3 Scan D {3} 3 {4} 1 {5} 3 Items 134 235 1235 25 C2 itemset sup L2 itemset sup 2 2 3 2 {1 {1 {1 {2 {2 {3 C3 itemset {2 3 5} Scan D {1 3} {2 3} {2 5} {3 5} 2} 3} 5} 3} 5} 5} 1 2 1 2 3 2 L1 itemset sup. {1} {2} {3} {5} 2 3 3 3 C2 itemset {1 2} Scan D {1 {1 {2 {2 {3 3} 5} 3} 5} 5} L3 itemset sup {2 3 5} 2 63 Naïve Algorithm: Apriori + Constraint Database D TID 100 200 300 400 itemset sup. C1 {1} 2 {2} 3 Scan D {3} 3 {4} 1 {5} 3 Items 134 235 1235 25 C2 itemset sup L2 itemset sup 2 2 3 2 {1 {1 {1 {2 {2 {3 C3 itemset {2 3 5} Scan D {1 3} {2 3} {2 5} {3 5} 2} 3} 5} 3} 5} 5} 1 2 1 2 3 2 L1 itemset sup. {1} {2} {3} {5} 2 3 3 3 C2 itemset {1 2} Scan D L3 itemset sup {2 3 5} 2 {1 {1 {2 {2 {3 3} 5} 3} 5} 5} Constraint: Sum{S.price < 5} 64 The Constrained Apriori Algorithm: Push an Anti-monotone Constraint Deep Database D TID 100 200 300 400 itemset sup. C1 {1} 2 {2} 3 Scan D {3} 3 {4} 1 {5} 3 Items 134 235 1235 25 C2 itemset sup L2 itemset sup 2 2 3 2 {1 {1 {1 {2 {2 {3 C3 itemset {2 3 5} Scan D {1 3} {2 3} {2 5} {3 5} 2} 3} 5} 3} 5} 5} 1 2 1 2 3 2 L1 itemset sup. {1} {2} {3} {5} 2 3 3 3 C2 itemset {1 2} Scan D L3 itemset sup {2 3 5} 2 {1 {1 {2 {2 {3 3} 5} 3} 5} 5} Constraint: Sum{S.price < 5} 65 The Constrained Apriori Algorithm: Push Another Constraint Deep Database D TID 100 200 300 400 itemset sup. C1 {1} 2 {2} 3 Scan D {3} 3 {4} 1 {5} 3 Items 134 235 1235 25 C2 itemset sup L2 itemset sup 2 2 3 2 {1 {1 {1 {2 {2 {3 C3 itemset {2 3 5} Scan D {1 3} {2 3} {2 5} {3 5} 2} 3} 5} 3} 5} 5} 1 2 1 2 3 2 L1 itemset sup. {1} {2} {3} {5} 2 3 3 3 C2 itemset {1 2} Scan D L3 itemset sup {2 3 5} 2 {1 {1 {2 {2 {3 3} 5} 3} 5} 5} Constraint: min{S.price <= 1 } 66 Chapter 5: Mining Association Rules in Large Databases Association rule mining Algorithms for scalable mining of (single-dimensional Boolean) association rules in transactional databases Mining various kinds of association/correlation rules Constraint-based association mining Sequential pattern mining Applications/extensions of frequent pattern mining Summary 67 Sequence Databases and Sequential Pattern Analysis Transaction databases, time-series databases vs. sequence databases Frequent patterns vs. (frequent) sequential patterns Applications of sequential pattern mining Customer shopping sequences: First buy computer, then CD-ROM, and then digital camera, within 3 months. Medical treatment, natural disasters (e.g., earthquakes), science & engineering processes, stocks and markets, etc. Telephone calling patterns, Weblog click streams DNA sequences and gene structures 68 What Is Sequential Pattern Mining? Given a set of sequences, find the complete set of frequent subsequences A sequence : < (ef) (ab) (df) c b > A sequence database SID sequence 10 <a(abc)(ac)d(cf)> 20 <(ad)c(bc)(ae)> 30 <(ef)(ab)(df)cb> 40 <eg(af)cbc> An element may contain a set of items. Items within an element are unordered and we list them alphabetically. <a(bc)dc> is a subsequence of <a(abc)(ac)d(cf)> Given support threshold min_sup =2, <(ab)c> is a sequential pattern 69 Challenges on Sequential Pattern Mining A huge number of possible sequential patterns are hidden in databases A mining algorithm should find the complete set of patterns, when possible, satisfying the minimum support (frequency) threshold be highly efficient, scalable, involving only a small number of database scans be able to incorporate various kinds of user-specific constraints 70 Studies on Sequential Pattern Mining Concept introduction and an initial Apriori-like algorithm R. Agrawal & R. Srikant. “Mining sequential patterns,” ICDE’95 GSP—An Apriori-based, influential mining method (developed at IBM Almaden) R. Srikant & R. Agrawal. “Mining sequential patterns: Generalizations and performance improvements,” EDBT’96 From sequential patterns to episodes (Apriori-like + constraints) H. Mannila, H. Toivonen & A.I. Verkamo. “Discovery of frequent episodes in event sequences,” Data Mining and Knowledge Discovery, 1997 Mining sequential patterns with constraints M.N. Garofalakis, R. Rastogi, K. Shim: SPIRIT: Sequential Pattern Mining with Regular Expression Constraints. VLDB 1999 71 Sequential pattern mining: Cases and Parameters Duration of a time sequence T Sequential pattern mining can then be confined to the data within a specified duration Ex. Subsequence corresponding to the year of 1999 Ex. Partitioned sequences, such as every year, or every week after stock crashes, or every two weeks before and after a volcano eruption Event folding window w If w = T, time-insensitive frequent patterns are found If w = 0 (no event sequence folding), sequential patterns are found where each event occurs at a distinct time instant If 0 < w < T, sequences occurring within the same period w are folded in the analysis 72 Example When event folding window is 5 munites Purchases within 5 munits is considered to be taken together 73 Sequential pattern mining: Cases and Parameters (2) Time interval, int, between events in the discovered pattern int = 0: no interval gap is allowed, i.e., only strictly consecutive sequences are found min_int int max_int: find patterns that are separated by at least min_int but at most max_int Ex. “Find frequent patterns occurring in consecutive weeks” Ex. “If a person rents movie A, it is likely she will rent movie B within 30 days” (int 30) int = c 0: find patterns carrying an exact interval Ex. “Every time when Dow Jones drops more than 5%, what will happen exactly two days later?” (int = 2) 74 A Basic Property of Sequential Patterns: Apriori A basic property: Apriori (Agrawal & Sirkant’94) If a sequence S is not frequent Then none of the super-sequences of S is frequent E.g, <hb> is infrequent so do <hab> and <(ah)b> Seq. ID Sequence 10 <(bd)cb(ac)> 20 <(bf)(ce)b(fg)> 30 <(ah)(bf)abf> 40 <(be)(ce)d> 50 <a(bd)bcb(ade)> Given support threshold min_sup =2 75 GSP—A Generalized Sequential Pattern Mining Algorithm GSP (Generalized Sequential Pattern) mining algorithm Outline of the method proposed by Agrawal and Srikant, EDBT’96 Initially, every item in DB is a candidate of length-1 for each level (i.e., sequences of length-k) do scan database to collect support count for each candidate sequence generate candidate length-(k+1) sequences from length-k frequent sequences using Apriori repeat until no frequent sequence or no candidate can be found Major strength: Candidate pruning by Apriori 76 Finding Length-1 Sequential Patterns Examine GSP using an example Initial candidates: all singleton sequences <a>, <b>, <c>, <d>, <e>, <f>, <g>, <h> Scan database once, count support for candidates min_sup =2 Cand Sup <a> 3 <b> 5 <c> 4 <d> 3 <e> 3 <f> 2 Seq. ID Sequence 10 <(bd)cb(ac)> <g> 1 20 <(bf)(ce)b(fg)> <h> 1 30 <(ah)(bf)abf> 40 <(be)(ce)d> 50 <a(bd)bcb(ade)> 77 Generating Length-2 Candidates 51 length-2 Candidates <a> <a> <b> <c> <d> <e> <f> <a> <b> <c> <d> <e> <f> <a> <aa> <ab> <ac> <ad> <ae> <af> <b> <ba> <bb> <bc> <bd> <be> <bf> <c> <ca> <cb> <cc> <cd> <ce> <cf> <d> <da> <db> <dc> <dd> <de> <df> <e> <ea> <eb> <ec> <ed> <ee> <ef> <f> <fa> <fb> <fc> <fd> <fe> <ff> <b> <c> <d> <e> <f> <(ab)> <(ac)> <(ad)> <(ae)> <(af)> <(bc)> <(bd)> <(be)> <(bf)> <(cd)> <(ce)> <(cf)> <(de)> <(df)> <(ef)> Without Apriori property, 8*8+8*7/2=92 candidates Apriori prunes 44.57% candidates 78 Generating Length-3 Candidates and Finding Length-3 Patterns Generate Length-3 Candidates Self-join length-2 sequential patterns Based on the Apriori property <ab>, <aa> and <ba> are all length-2 sequential patterns <aba> is a length-3 candidate <(bd)>, <bb> and <db> are all length-2 sequential patterns <(bd)b> is a length-3 candidate 46 candidates are generated Find Length-3 Sequential Patterns Scan database once more, collect support counts for candidates 19 out of 46 candidates pass support threshold 80 The GSP Mining Process 5th scan: 1 cand. 1 length-5 seq. pat. Cand. cannot pass sup. threshold <(bd)cba> Cand. not in DB at all 4th scan: 8 cand. 6 length-4 seq. <abba> <(bd)bc> … pat. 3rd scan: 46 cand. 19 length-3 seq. <abb> <aab> <aba> <baa> <bab> … pat. 20 cand. not in DB at all 2nd scan: 51 cand. 19 length-2 seq. <aa> <ab> … <af> <ba> <bb> … <ff> <(ab)> … <(ef)> pat. 10 cand. not in DB at all 1st scan: 8 cand. 6 length-1 seq. <a> <b> <c> <d> <e> <f> <g> <h> pat. min_sup =2 Seq. ID Sequence 10 <(bd)cb(ac)> 20 <(bf)(ce)b(fg)> 30 <(ah)(bf)abf> 40 <(be)(ce)d> 50 <a(bd)bcb(ade)> 81 Definition c is a contiguous subsequence of a sequence s:{s1,s2,...,sn} if c is derived by dropping an item from s1 or sn c is derived by dropping an item from si which has at least 2 items c’ is a contiguous subsequence of c and c is a contiguous subsequence of s Ex: s:{ (1,2),(3,4),5,6} { 2,(3,4),5}, { (1,2),3,5,6},{ (3,5} are but { (1,2),(3,4),6},{ (1,5,6} are not 82 Candidate genration Step 1: Join Step Lk-1 join with Lk-1 to give Ck s1 and s2 are joined if dropping first item of s1 and last item of s2 gives the same sequence s1 is extended by adding the last item of s2 Step 2: Prune Step Delete candidate sequences having (k-1) contiguous subsequences whose support count is less than min_support count 83 L3 {(1,2),3} {(1,2),4} {1,(3,4)} {(1,3),5} {2,(3,4)} {2,3,5} C4 {(1,2),(3,4)} {(1,2),3,5} L4 {(1,2),(3,4)} {(1,2),3} joined with {2,(3,4)} to give {(1,2),(3,4)} {(1,2),3} joined with {2,3,5} to give {(1,2),3,5} {(1,2),3,5} is dropped since its 3 contiguous subseq {(1,3,5} not in L3 84 Bottlenecks of GSP A huge set of candidates could be generated 1,000 frequent length-1 sequences generate 1000 999 1000 1000 1,499 ,500 length-2 candidates! 2 Multiple scans of database in mining Real challenge: mining long sequential patterns An exponential number of short candidates A length-100 sequential pattern needs 1030 candidate sequences! 100 100 100 2 i 1 i 1 1030 86