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Data Mining: Principles and Algorithms Mining Sequence Patterns Jiawei Han Department of Computer Science Data Mining: Principles and Algorithms Mining Sequence Patterns Jiawei Han Department of Computer Science University of Illinois at Urbana-Champaign www. cs. uiuc. edu/~hanj © 2012 Jiawei Han. All rights reserved. 1

15 March 2018 Data Mining: Concepts and Techniques 2 15 March 2018 Data Mining: Concepts and Techniques 2

Sequence Databases & Sequential Patterns n n n Transaction databases, time-series databases vs. sequence Sequence Databases & Sequential Patterns n n n Transaction databases, time-series databases vs. sequence databases Frequent patterns vs. (frequent) sequential patterns Applications of sequential pattern mining n Customer shopping sequences: n First buy computer, then CD-ROM, and then digital camera, within 3 months. n Medical treatments, natural disasters (e. g. , earthquakes), science & eng. processes, stocks and markets, etc. n Telephone calling patterns, Weblog click streams n Program execution sequence data sets n DNA sequences and gene structures 3

What Is Sequential Pattern Mining? n Given a set of sequences, find the complete What Is Sequential Pattern Mining? n Given a set of sequences, find the complete set of frequent subsequences A sequence : < (ef) (ab) (df) c b > A sequence database SID 10 20 30 40 sequence <(ad)c(bc)(ae)> <(ef)(ab)(df)cb> An element may contain a set of items. Items within an element are unordered and we list them alphabetically. is a subsequence of Given support threshold min_sup =2, <(ab)c> is a sequential pattern 4

Challenges on Sequential Pattern Mining n n A huge number of possible sequential patterns Challenges on Sequential Pattern Mining n n A huge number of possible sequential patterns are hidden in databases A mining algorithm should n n n 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 5

Sequential Pattern Mining Algorithms n Concept introduction and an initial Apriori-like algorithm n n Sequential Pattern Mining Algorithms n Concept introduction and an initial Apriori-like algorithm n n Agrawal & Srikant. Mining sequential patterns, ICDE’ 95 Apriori-based method: GSP (Generalized Sequential Patterns: Srikant & Agrawal @ EDBT’ 96) n Pattern-growth methods: Free. Span & Prefix. Span (Han et al. @KDD’ 00; Pei, et al. @ICDE’ 01) n Vertical format-based mining: SPADE ([email protected] Leanining’ 00) n Constraint-based sequential pattern mining (SPIRIT: Garofalakis, Rastogi, [email protected]’ 99; Pei, Han, Wang @ CIKM’ 02) n Mining closed sequential patterns: Clo. Span (Yan, Han & Afshar @SDM’ 03) 6

The Apriori Property of Sequential Patterns n A basic property: Apriori (Agrawal & Sirkant’ The Apriori Property of Sequential Patterns n A basic property: Apriori (Agrawal & Sirkant’ 94) n If a sequence S is not frequent n Then none of the super-sequences of S is frequent n E. g, is infrequent so do and <(ah)b> Seq. ID Sequence 10 <(bd)cb(ac)> 20 <(bf)(ce)b(fg)> 30 <(ah)(bf)abf> 40 <(be)(ce)d> 50 Given support threshold min_sup =2 7

GSP—Generalized Sequential Pattern Mining n n n GSP (Generalized Sequential Pattern) mining algorithm n GSP—Generalized Sequential Pattern Mining n n n GSP (Generalized Sequential Pattern) mining algorithm n proposed by Agrawal and Srikant, EDBT’ 96 Outline of the method n Initially, every item in DB is a candidate of length-1 n for each level (i. e. , sequences of length-k) do n scan database to collect support count for each candidate sequence n generate candidate length-(k+1) sequences from length-k frequent sequences using Apriori n repeat until no frequent sequence or no candidate can be found Major strength: Candidate pruning by Apriori 8

Candidate Generate-and-test: Drawbacks n A huge set of candidate sequences generated n n Especially Candidate Generate-and-test: Drawbacks n A huge set of candidate sequences generated n n Especially 2 -item candidate sequence Multiple Scans of database needed n The length of each candidate grows by one at each database scan n Inefficient for mining long sequential patterns n A long pattern grow up from short patterns n The number of short patterns is exponential to the length of mined patterns 12

The SPADE Algorithm n SPADE (Sequential PAttern Discovery using Equivalent Class) developed by Zaki The SPADE Algorithm n SPADE (Sequential PAttern Discovery using Equivalent Class) developed by Zaki 2001 n A vertical format sequential pattern mining method n A sequence database is mapped to a large set of n n Item: Sequential pattern mining is performed by n growing the subsequences (patterns) one item at a time by Apriori candidate generation 13

The SPADE Algorithm 14 The SPADE Algorithm 14

Bottlenecks of GSP and SPADE n A huge set of candidates could be generated Bottlenecks of GSP and SPADE n A huge set of candidates could be generated n 1, 000 frequent length-1 sequences generate s huge number of length-2 candidates! n Multiple scans of database in mining n Breadth-first search n Mining long sequential patterns n Needs an exponential number of short candidates n A length-100 sequential pattern needs 1030 candidate sequences! 15

Efficiency of Prefix. Span n No candidate sequence needs to be generated n Projected Efficiency of Prefix. Span n No candidate sequence needs to be generated n Projected databases keep shrinking n Major cost of Prefix. Span: Constructing projected databases n Can be improved by pseudo-projections 20

Pseudo-Projection vs. Physical Projection n Pseudo-projection avoids physically copying postfixes n n However, it Pseudo-Projection vs. Physical Projection n Pseudo-projection avoids physically copying postfixes n n However, it is not efficient when database cannot fit in main memory n n Efficient in running time and space when database can be held in main memory Disk-based random accessing is very costly Suggested Approach: n n Integration of physical and pseudo-projection Swapping to pseudo-projection when the data set fits in memory 22

Performance of Sequential Pattern Mining Algorithms Performance comparison on data set C 10 T Performance of Sequential Pattern Mining Algorithms Performance comparison on data set C 10 T 8 S 8 I 8 Performance comparison on Gazelle data set Performance comparison: with pseudo-projection vs. without pseudo-projection 23

Clo. Span: Mining Closed Sequential Patterns n n n A closed sequential pattern s: Clo. Span: Mining Closed Sequential Patterns n n n A closed sequential pattern s: there exists no superpattern s’ such that s’ כ s, and s’ and s have the same support Which one is closed? : 20, : 15 Why mine close seq. patterns? n n n Reduces the number of (redundant) patterns but attains the same expressive power Property: If s’ כ s, closed iff two project DBs have the same size Using Backward Subpattern and Backward Superpattern pruning to prune redundant search space 24

Performance Comparison: Clo. Span vs. Prefix. Span 25 Performance Comparison: Clo. Span vs. Prefix. Span 25

Constraint-Based Seq. -Pattern Mining n n Constraint-based sequential pattern mining n Constraints: User-specified, for Constraint-Based Seq. -Pattern Mining n n Constraint-based sequential pattern mining n Constraints: User-specified, for focused mining of desired patterns n How to explore efficient mining with constraints? — Optimization Classification of constraints n Anti-monotone: E. g. , value_sum(S) < 150, min(S) > 10 n Monotone: E. g. , count (S) > 5, S {PC, digital_camera} n Succinct: E. g. , length(S) 10, S {Pentium, MS/Office, MS/Money} n Convertible: E. g. , value_avg(S) < 25, profit_sum (S) > 160, max(S)/avg(S) < 2, median(S) – min(S) > 5 n Inconvertible: E. g. , avg(S) – median(S) = 0 26

From Sequential Patterns to Structured Patterns n n Sets, sequences, trees, graphs, and other From Sequential Patterns to Structured Patterns n n Sets, sequences, trees, graphs, and other structures n Transaction DB: Sets of items n {{i 1, i 2, …, im}, …} n Seq. DB: Sequences of sets: n {<{i 1, i 2}, …, {im, in, ik}>, …} n Sets of Sequences: n {{, …, }, …} n Sets of trees: {t 1, t 2, …, tn} n Sets of graphs (mining for frequent subgraphs): n {g 1, g 2, …, gn} Mining structured patterns in XML documents, biochemical structures, etc. 27

Alternative I: Episodes and Episode Pattern Mining n Alternative patterns: Episodes and regular expressions Alternative I: Episodes and Episode Pattern Mining n Alternative patterns: Episodes and regular expressions n n Parallel episodes: A & B n n Serial episodes: A B Regular expressions: (A|B)C*(D E) Methods for episode pattern mining n Method 1: Variations of Apriori/GSP-like algorithms n Method 2: Projection-based pattern growth n n Can you work out the details? Question: What is the difference between mining episodes and constraint-based pattern mining? 28

Ref: Mining Sequential Patterns n n n n n R. Srikant and R. Agrawal. Ref: Mining Sequential Patterns n n n n n R. Srikant and R. Agrawal. Mining sequential patterns: Generalizations and performance improvements. EDBT’ 96. H. Mannila, H Toivonen, and A. I. Verkamo. Discovery of frequent episodes in event sequences. DAMI: 97. M. Zaki. SPADE: An Efficient Algorithm for Mining Frequent Sequences. Machine Learning, 2001. J. Pei, J. Han, H. Pinto, Q. Chen, U. Dayal, and M. -C. Hsu. Prefix. Span: Mining Sequential Patterns Efficiently by Prefix-Projected Pattern Growth. ICDE'01 (TKDE’ 04). J. Pei, J. Han and W. Wang, Constraint-Based Sequential Pattern Mining in Large Databases, CIKM'02. X. Yan, J. Han, and R. Afshar. Clo. Span: Mining Closed Sequential Patterns in Large Datasets. SDM'03. J. Wang and J. Han, BIDE: Efficient Mining of Frequent Closed Sequences, ICDE'04. H. Cheng, X. Yan, and J. Han, Inc. Span: Incremental Mining of Sequential Patterns in Large Database, KDD'04. J. Han, G. Dong and Y. Yin, Efficient Mining of Partial Periodic Patterns in Time Series Database, ICDE'99. J. Yang, W. Wang, and P. S. Yu, Mining asynchronous periodic patterns in time series data, KDD'00. 29

Research Problems n Mining repetitive sequential patterns in Seq. DBs with mixed long and Research Problems n Mining repetitive sequential patterns in Seq. DBs with mixed long and short sequences n n Ding et al. ’s ICDE’ 09 paper Exploring applications of sequential pattern mining n Mining sequential patterns in text documents? Will sequential pattern be more powerful than n-grams? n Efficient mining of colossal sequential patterns? n Mining approximate sequential patterns? n Classification using sequential patterns? n Clustering with sequential patterns? n Exploring sequential pattern mining for biological data analysis 30

15 March 2018 Data Mining: Concepts and Techniques 31 15 March 2018 Data Mining: Concepts and Techniques 31