Approach to Data Mining from Algorithm and Computation Takeaki Uno, ETH Switzerland, NII Japan Hiroki Arimura, Hokkaido University, Japan.

Approach to Data Mining from Algorithm and Computation Takeaki Uno, ETH Switzerland, NII Japan Hiroki Arimura, Hokkaido University, Japan

Frequent Pattern Mining ・・ Data mining is an important tool for analysis of data in many scientific and industrial areas ・・ The aim of data mining is to “find interesting, or valuable something” ・・ But, we don’t know what is interesting, nor is valuable ・・ So, we give some criteria that would be satisfied by interesting or valuable something, and find all patterns satisfying them.

Image of Pattern Mining ・・ Pattern mining is a problem of find all patterns from the given (possibly structured) database satisfying the given constraints H H C C C C H H H O O N H H H H chemical compounds XML database databases name C C C person agephone name family person name person age C O O H extract interesting patterns Frequent pattern mining is an enumeration problem of all patterns appearing frequently, at least given threshold in the database person family C H H

Approach from… ・・ In real world, the inputs database is usually huge, and the output patterns are so huge, thus efficient computation is very important ・・ Many research are done, but many of them are based on “database, data engineering, modeling”, not algorithm. Ex.) How to make data compressed, how to execute query fast, which model is good, etc… ・・ Here we see want to separate the problems; from algorithmic view, what is important?, what we can do?

Distinguish the Focus, Problems ・・ “my algorithm is very fast for these datasets”, - but the data is very artificial, or including few items… ・・ “the algorithm might not work for huge datasets, if it”, - difficult to be fast for both small and huge ・・ We would like to distinguish the techniques and problems; - - scalability - - I/O - - Huge datasets - - Data compression ・・ The techniques would be “orthogonal”

Approach from… ・・ Many research are done, but many of them are based on “database, data engineering, modeling”, not algorithm. Ex.) How to make data compressed, how to execute query fast, which model is good, etc… ・・ Here we see the problems as “enumeration problems”, and try to clarify what kind of techniques are important for efficient computation, with examples on itemset mining Good Models What techniques enlarge the solvable models toward the good models Solvable Models

From the Algorithm Theory… ・・ Here we focus only on algorithms, thus topics are - - output sensitive computation time (bad, if long time for small output) - - memory use should depend only on input size - - computation time for an iteration - - reduce the input of each iteration This is so important!!! Good Models What techniques enlarge the solvable models toward the good models Solvable Models

From the Algorithm Theory… ・・ Here we focus only on the case that the input fits the memory - - scalability: output sensitive computation time (bad, if long time for small output) - - memory use should depend only on input size - - computation time for an iteration - - reduce the input of each iteration (from bottom wideness) This is so important!!! TIME #iterations time of an iteration = × + I/O

Bottom Wideness ・・ Enumeration algorithms usually have recursive tree structures, there are many iterations in deeper levels Size = time Procedure to reduce input of recursive calls

Bottom Wideness ・・ Enumeration algorithms usually have recursive tree structures, there are many iterations in deeper levels Total computation time will be half only by one reduction for input Size = time Procedure to reduce input of recursive calls

Bottom Wideness ・・ Enumeration algorithms usually have recursive tree structures, there are many iterations in deeper levels Total computation time will be half only by one reduction for input Size = time Procedure to reduce input of recursive calls Recursively reduce the input  computation time is much reduced

Advantage of Bottom Wideness ・・ Suppose that the recursion tree has iterations exponentially many on lower levels (ex. (2 × #level i) ≦ #level i+1 O(n3)O(n3) O(1) recursion tree amortized computation time is O(1) for each output !!

Advantage of Bottom Wideness ・・ Suppose that the recursion tree has iterations exponentially many on lower levels (ex. (2 × #level i) ≦ #level i+1 O(n5)O(n5) O(n) recursion tree amortized computation time is O(n) for each output !! Computation time for each output depends only on bottom levels:   reduce the computation time on lower levels by reduction of input Computation time for each output depends only on bottom levels:   reduce the computation time on lower levels by reduction of input

Frequent Itemset Mining 1,2,5,6,7 2,3,4,5 1,2,7,8,9 1,7,9 2,7,9 2 ＝ D＝＝ D＝・ Transaction database : ・ Transaction database D : transactionsitemset a database composed of transactions defined on itemset E i.e., ∀ t ∈ D, t ⊆ E - - basket data - - links of web pages - - words in documents ・ itemset ・ A subset P of E is called an itemset occurrence: occurrence of P : a transaction in D including P denotation : denotation Occ(P) of P : set of occurrences of P ・ frequency ・ |Occ(P)| is called frequency of P denotation of {1,2} ＝＝ {1,2,5,6,7,9}, {1,2,7,8,9}

Frequent Itemset 1,2,5,6,7,9 2,3,4,5 1,2,7,8,9 1,7,9 2,7,9 2 T ＝ patterns included in at least 3 transactions at least 3 transactions {1} {2} {7} {9} {1,7} {1,9} {2,7} {2,9} {7,9} {1,7,9} {2,7,9} ・ minimum support ・ Given a minimum support θ, Frequent itemset ≧ Frequent itemset: an itemset s.t. (frequency) ≧ θ (a subset of items, which is included in at least θ transactions)Ex.)

Techniques for Efficient Mining ・・ There are many techniques for fast mining - - apriori - - backtracking - - down project - - pruning by infrequent subset - - bitmap - - occurrence deliver - - FP-tree (trie, prefix tree) - - filtering (unification) - - conditional (projected) database - - trimming of database for search strategy for speeding up iterations for database reduction & bottom wideness

Search Strategies ・・ Frequent Itemsets form a connected component on itemset lattice - - Apriori algorithms generate itemsets level-by-level  pruning by infrequent subsets  much memory use - - Backtracking algorithms generate itemset in depth-first manner  small memory use  match down project, etc. φ 12 34 1,31,23,42,41,42,3 1,2,31,2,41,3,42,3,4 1,2,3,4 frequent Apriori uses long time much memory when output is large

Set k := 0, O k := {φ} While (O k ≠φ) { for each P ∪ e, P ∈ O k { if all P ∪ e-f ∈ O k then compute Occ(P ∪ e) if |Occ(P ∪ e)| ≧ θthen O k+1  P ∪ e } k := k+1 }Backtracking φ 12 34 1,31,23,42,41,42,3 1,2,31,2,41,3,42,3,4 1,2,3,4 frequent apriori Backtrack (P, Occ(P)) for each e>tail(P) { compute Occ(P ∪ e) if |Occ(P ∪ e)| ≧ θthen Backtrack ( P ∪ e, Occ(P ∪ e) ) } backtracking

Speeds Iteration Bottleneck in iteration is computing Occ(P ∪ e) - - down project: Occ(P ∪ e) = Occ(P ∪ e) ∩ Occ(e)  O(||D ≧ P ||): the size of database of the part larger than tail(P) - - pruning by infrequent subset +  |P| search query + O(c × ||D ≧ P ||) - - bitmap: compute Occ(P ∪ e) ∩ Occ(e) by AND operation  (n -tail(P)) × m/32 operations - - occurrence deliver: comp. Occ(P ∪ e) for all e by one scan of D(P) ≧ P  O(||D(P) ≧ P ||) : D(P) is transactions including P ||D|| m n bitmap is slow if database is sparse, pruning is slow for huge output occurrence deliver is fast if threshold (minimum support) is small bitmap is slow if database is sparse, pruning is slow for huge output occurrence deliver is fast if threshold (minimum support) is small

Occurrence Deliver ・・ Compute the denotations of P ∪ {i} for all i’s at once, 1,2,5,6,7,9 2,3,4,5 1,2,7,8,9 1,7,9 2,7,9 2 ＝D＝＝D＝ A125679 B2345 C12789 D179 E279 F2 P = {1,7} AAA CC Check the frequency for all items to be added in linear time of the database size Check the frequency for all items to be added in linear time of the database size A C D Generating the recursive calls in reverse direction, we can re-use the memory Generating the recursive calls in reverse direction, we can re-use the memory

Database Reductions Conditional database is to reduce database by unnecessary items and transactions, for deeper levels 3 5 56 1,3,5 1,2,5,6 1,4,6 1,2,6 3,5 5,6 6 θ = 3 Remove infrequent items, items included in all filtering Unify same transactions 3,5 ×3 5,6 6 ×2 6 1 Remove infrequent items, automatically unified FP-tree, prefix tree Linear time O(||D||log ||D||) time Compact if database is dense and large

Summary of Techniques ・・ Database is dense and large even for bottom levels of computation  support is large ・・ #output solutions is huge  support is smallPrediction: - - apriori will be slow when support is small - - conditional database is fast when support is small - - bitmap will be slow for sparse datasets - - FP-tree will be bit slow for sparse datasets, and fast for large support ・・ Database is dense and large even for bottom levels of computation  support is large ・・ #output solutions is huge  support is smallPrediction: - - apriori will be slow when support is small - - conditional database is fast when support is small - - bitmap will be slow for sparse datasets - - FP-tree will be bit slow for sparse datasets, and fast for large support

Results from FIMI 04 (sparse datasets) Conditional database is good, bitmap is slow FP-tree  large support, occurrence deliver  small support Conditional database is good, bitmap is slow FP-tree  large support, occurrence deliver  small support bitmap apriori cond. O(n) vs O(nlogn) FP-tree

Results on Dense Datasets Apriori is still slow for middle supports, FP-tree is good Apriori is still slow for middle supports, FP-tree is good apriori FP-tree cond. FP-tree, cond bitmap #nodes in FP-tree = (||D|| filtered)/6 #nodes in FP-tree = (||D|| filtered)/6

Summary on Computation ・・ We can understand the reason of efficiency from algorithmic view - - reduce the input of each iteration according to bottom wideness - - reduce the computation time for an iteration (probably, combination of conditional database, patricia tree, and occurrence deliver will be good) ・・ We can observe similarly other pattern mining problems, sequence mining, string mining, tree mining, graph mining, etc. ・・ We can understand the reason of efficiency from algorithmic view - - reduce the input of each iteration according to bottom wideness - - reduce the computation time for an iteration (probably, combination of conditional database, patricia tree, and occurrence deliver will be good) ・・ We can observe similarly other pattern mining problems, sequence mining, string mining, tree mining, graph mining, etc. Next we see closed pattern which represents some similar patterns, we begin with itemsets Next we see closed pattern which represents some similar patterns, we begin with itemsets

Modeling: Closed Itemsets Modeling: Closed Itemsets [Pasquier et. al. 1999] ・・ Usually, #frequent itemsets is huge, when we mine in depth   we want to decrease itemsets in some way ・・ There are many ways for this task, ex., giving some scores, group similar itemsets, looking at the other parameters, etc. But, we would like to approach from theory closed patterns Here we introduce closed patterns ・・ Consider the itemsets having the same denotations   we would say “they have the same information” ・ closed pattern ・ we focus only on the maximal among them, called closed pattern = (= intersection of occurrences in the denotation)

Example of Closed Itemset 1,2,5,6,7,9 2,3,4,5 1,2,7,8,9 1,7,9 2,7,9 2 T ＝ patterns included in at least 3 transactions at least 3 transactions {1} {2} {7} {9} {1,7} {1,9} {2,7} {2,9} {7,9} {1,7,9} {2,7,9} ・・ In general, #frequent itemsets ≦ #frequent closed itemsets Especially, “<<” holds if database has some structures (Databases with some structure tend to have huge frequent itemsets, thus this is an advantage)

Difference of #itemsets #frequent itemsets << #closed itemsets when threshold θis small #frequent itemsets << #closed itemsets when threshold θis small

Closure Extension of Itemset ・・ Usual backtracking does not work for closed itemsets, because there are possibly big gap between closed itemsets ・・ On the other hand, any closed itemset is obtained from another by “add an item and take closure (maximal)” - - closure of P is the closed itemset having the same denotation to P, and computed by taking intersection of Occ(P) This is an adjacency structure defined on closed itemsets, thus we can perform graph search on it, with using memory φ 1,3 1,2 1,2,31,2,41,3,42,3,4 1 2 34 3,42,41,42,3 1,2,3,4

PPC extension ・・ Closure extension gives us an acyclic adjacency structure for us, but it’s not enough to get a memory efficient algorithm (we need to store discovered itemsets in memory) ・・ We introduce PPC extension to obtain tree structure A closure extension P’ obtained from P+e is a PPC extension  prefixes of P’ and P are the same (smaller than e) PPC extension ・・ Any closed itemset is a PPC extension of just one other closed itemset

Example of PPC Extension ・・ closure extension   acyclic ・・ ppc extension   tree ・・ closure extension   acyclic ・・ ppc extension   tree closure extension ppc extension 1,2,5,6,7,9 2,3,4,5 1,2,7,8,9 1,7,9 2,7,9 2 ＝D ＝＝D ＝ φ {1,7,9} {2,7,9} {1,2,7,9} {7,9} {2,5} {2} {2,3,4,5} {1,2,7,8,9}{1,2,5,6,7,9}

Example of PPC Extension (1,2,7,9), (1,2,7), (1,2) ⊆ {1,2,5,6,7,9}, {1,2,7,8,9} (1,7,9), (1,7), (1) ⊆ {1,7,9}, {1,2,5,6,7,9}, {1,2,7,8,9} (1,2,7,9), (1,2,7), (1,2) ⊆ {1,2,5,6,7,9}, {1,2,7,8,9} (1,7,9), (1,7), (1) ⊆ {1,7,9}, {1,2,5,6,7,9}, {1,2,7,8,9} 1,2,5,6,7,9 2,3,4,5 1,2,7,8,9 1,7,9 2,7,9 2 ＝D ＝＝D ＝ φ {1,7,9} {2,7,9} {1,2,7,9} {7,9} {2,5} {2} {2,3,4,5} {1,2,7,8,9}{1,2,5,6,7,9}

For Efficient Computation ・・ Computation of closure takes long time ・・ We use database reduction, from the fact that “if P’ is PPC extension by P+e, and P’’ is PPC extension by P’+f then e < f ”, thus prefix is used only for intersection! 1,2,5,6,7,9 2,3,4,5 1,2,5,7,8,9 1,5,6,7 2,5,7,9 2,3,5,6,7,8 e = 5e = 5 1,2,5,6,7,9 2,5 1,2,5,7,9 1,5,6,7 2,5,7,9 2,5,6,7 1,2,5,6,7,9 2,5,7,9 × 2 5,6,7 × 2

Experiment: vs. Frequent Itemset (sparse) Computation time/itemset is very stable There is no big difference of computation time Computation time/itemset is very stable There is no big difference of computation time

Experiment: vs. Frequent Itemset (dense) Computation time/itemset is very stable There is no big difference of computation time Computation time/itemset is very stable There is no big difference of computation time

Compare to Other Methods ・・ There are roughly two methods to enumerate closed patterns frequent pattern base: enumerate all frequent patterns, and output only closed ones (+ some pruning), check closedness by keeping all discovered itemsets in memory closure base closure base: compute closed pattern by closure, and avoid the duplication by keeping all discovered itemsets in memory If #solution is small, frequent pattern base is fast, since search for checking closedness takes very short time

vs. Other Implementations (sparse) Large minimum support  frequent pattern base Small minimum support  PPC extension Large minimum support  frequent pattern base Small minimum support  PPC extension

vs. Other Implementations (dense) Small minimum support  PPC extension and database reduction are good

Extend Closed Patterns ・・ There are several mining problems for which we can introduce closed patterns (union of occurrences is unique!!) - - Ranked trees (labeled trees without siblings of the same label) - - Motifs (string with wildcards) For these problems, PPC extension also works similarly, with conditional database and occurrence deliver A BA AAB C AB??EF?H ABCDEFGH ABZZEFZH

Conclusion ・・ We overviews the techniques on frequent pattern mining as enumeration algorithms, and show that - - complexity of one iteration and bottom wideness are important ・・ We show that closed pattern is probably a valuable model, and can be enumerated efficiently ・・ Develop efficient algorithms and implementations for other basic mining problems ・・ Extend the class of problems in which closed patterns work well ・・ Develop efficient algorithms and implementations for other basic mining problems ・・ Extend the class of problems in which closed patterns work well Future works ABCD ACBD

Approach to Data Mining from Algorithm and Computation Takeaki Uno, ETH Switzerland, NII Japan Hiroki Arimura, Hokkaido University, Japan.

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Approach to Data Mining from Algorithm and Computation Takeaki Uno, ETH Switzerland, NII Japan Hiroki Arimura, Hokkaido University, Japan.

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Presentation on theme: "Approach to Data Mining from Algorithm and Computation Takeaki Uno, ETH Switzerland, NII Japan Hiroki Arimura, Hokkaido University, Japan."— Presentation transcript:

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