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Mining Frequent Itemsets from Uncertain Data Presented by Chun-Kit Chui, Ben Kao, Edward Hung Department of Computer Science, The University of Hong Kong PAKDD 2007 2009-04-10 Summarized by Jaeseok Myung Reference Slides : i.cs.hku.hk/~ckchui/kit/modules/getfiles.php?file=2007-5-23%20PAKDD2007.ppt

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Copyright 2009 by CEBT Contents Introduction Existential Uncertain Dataset Calculating the Expected Support – From uncertain dataset Contribution The U-Apriori Algorithm Data Trimming Framework – Dealing with computational issues of the U-Apriori algorightm Experiments Conclusion Center for E-Business Technology

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Copyright 2009 by CEBT Existential Uncertain Dataset An existential uncertain dataset is a transaction dataset in which each item is associated with an existential probability indicating the probability that the item “exists” in the transaction Center for E-Business Technology Existential Uncertain Dataset Traditional Transaction Dataset

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Copyright 2009 by CEBT Existential Uncertain Dataset In many applications, the existence of an item in a transaction is best captured by a likelihood measure or a probability Symptoms, being subjective observations, would best be represented by probabilities that indicate their presence The likelihood of presence of each symptom is represented in terms of existential probabilities Center for E-Business Technology Psychological Symptoms Dataset

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Copyright 2009 by CEBT Association Analysis The psychologists maybe interested in the associations between different symptoms Mood Disorder => Eating Disorder + Depression Association Analysis from Uncertain Dataset A core step is the extraction of frequent itemsets The occurrence frequency is often expressed in terms of a support However, the definition of support needs to be redefined Center for E-Business Technology Psychological Symptoms Dataset

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Copyright 2009 by CEBT Possible World Interpretation A dataset with two psychological symptoms and two patients 16 possible world in total The support counts of itemsets are well defined in each individual world Center for E-Business Technology Psychological symptoms dataset From the dataset, one possibility is that both patients are actually having both psychological illnesses On the other hand, the uncertain dataset also captures the possibility that patient 1 only has eating disorder while patient 2 has both of the illnesses

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Copyright 2009 by CEBT Possible World Interpretation Support of Itemset {Depression, Eating Disorder} Center for E-Business Technology Psychological symptoms dataset We can discuss the support count of the itemset {S1, S2} in possible world 1 0.2016 0.9 × 0.8 × 0.4 × 0.7 1 1 1 1 1 1 0 0.0504 0.3024 0.0864 0.1296 0.0056 0.0336 0.0224 0 We can also discuss the likelihood of possible world 1 being the true world The same process can be applied for all possible world

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Copyright 2009 by CEBT Expected Support To calculate expected support, we need to consider all possible worlds and obtain the weighted support in each of the enumerated possible world Center for E-Business Technology 2 0.2016 1 1 1 1 1 1 0 0.0504 0.3024 0.0864 0.1296 0.0056 0.0336 0.0224 0 Weighted Support 0.4032 0.0224 0.0504 0.3024 0.0864 0.1296 0.0056 0 0 Expected Support1 Weighted support can be calculated for each possible world Expected support can be calculated by summing up the weighted support of all the possible worlds We expect there will be 1 patient has both illnesses

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Copyright 2009 by CEBT Simplified Calculation of Expected Support Instead of enumerating all “Possible Worlds” to calculate the exp ected support, it can be calculated by scanning the uncertain dat aset once only Center for E-Business Technology Psychological symptoms database Weighted Support of {S1,S2} 0.72 0.28 Expected Support of {S1,S2} 1 Where P ti (x j ) is the existential probability of item x j in transaction t j The expected support of {S1, S2} can be calculated by multiplying the existential probabilities within the transaction and obtain the total sum of all transactions

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Copyright 2009 by CEBT Problem Definition Given an existential uncertain dataset D with each item of a tra nsaction associated with an existential probability, and a user -specified support threshold s, return ALL the itemsets having ex pected support greater than or equal to | D |× s. Introduction Existential Uncertain Dataset Calculating the Expected Support Value Contribution The U-Apriori Algorithm Data Trimming Framework Experiments Conclusion Center for E-Business Technology

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Copyright 2009 by CEBT The Apriori Algorithm Center for E-Business Technology Candidates {A} {B} {C} {D} {E} The Apriori algorithm starts from size-1 candidate items Candidates Subset Function X The Subset Function scans the dataset once and obtain the support count of All size-1 candidates If item {A} is infrequent, all itemsets including {A} cannot be a candidate The Subset Function scans the dataset once and obtain the support count of All size-1 candidates If item {A} is infrequent, all itemsets including {A} cannot be a candidate Large Itemsets {B} {C} {D} {E} XX XX X X X XX X XXXX X Apriori-Gen {BC} {BD} {BE} {CD} {CE} {DE} The Apriori-Gen procedure generates only those size-(k+1) candidates which are potentially frequent X XX X X X The algorithm iteratively prunes and verifies the candidates, until no candidates are generated

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Copyright 2009 by CEBT The U-Apriori Algorithm In uncertain dataset, each item is associated with an existential probability The Subset-Function reads the dataset transaction by transaction to update the support counts of the candidates The expected support of {1, 2} contributed by transaction 1 is 0.9 * 0.8 = 0.72 Center for E-Business Technology Apriori-Gen Candidates Large itemsets Subset Function 1 (90%)2 (80%)4 (5%)5 (60%)8 (0.2%) Candidate Itemset Expected Support {1,2}0 {1,5} {1,8} {4,5} {4,8} 0.72 0.54 0.0018 0.03 0.0001 Transaction 1 Other processes are same to the original apriori algorithm The authors call this minor modified algorithm the U-Apriori algorithm Inherited from the Apriori algorithm, U-Apriori does not scale well on large datasets If the expected support is too small, all resources are wasted Inherited from the Apriori algorithm, U-Apriori does not scale well on large datasets If the expected support is too small, all resources are wasted

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Copyright 2009 by CEBT Computational Issue Center for E-Business Technology CPU cost in each iteration of different dataset s Fraction of items with low existential probabili ty : 75% Iterations 0%33.33%50%60%66.67%75%71.4% 1234567 7 synthetic datasets with same frequent itemsets Vary the percentages of items with low existential probability (R) in the datasets Although all datasets contain the same frequent itemsets, U- Apriori algorithm requires different amount of time to execute We can potentially reduce insignificant support calculation

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Copyright 2009 by CEBT Data Trimming Framework In order to deal with the computational issue, we can create a trimmed dataset by trimming out all items with low existential probabilities During the trimming process, some statistics are kept for error estimation when mining the trimmed dataset Total expected support count being trimmed of each item. Maximum existential probability being trimmed of each item. Other information : e.g. inverted lists, signature files …etc Center for E-Business Technology I1I2 t190%80% t280%4% t32%5% t45%95% t594%95% Uncertain dataset I1I2 t190%80% t280% t495% t594%95% + Statistics Total expected support count being trimmed Maximum existential probability being trimmed I11.15% I21.23% Trimmed dataset

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Copyright 2009 by CEBT Data Trimming Framework Center for E-Business Technology Trimming Module Trimming Module Original Dataset The uncertain database is first passed into the trimming module to remove the items with low existential probability and gather statistics during the trimming process Uncertain Apriori Uncertain Apriori Trimmed Dataset The trimmed dataset is then mined by the U-Apriori algorithm Infrequent k-itemsets Pruning Module Pruning Module Statistics The infrequent itemsets pruned by the U-Apriori algorithm can be a mistake The pruning module uses the statistics gathered from the trimming module in order to find out whether the itemsets are infrequent in the original dataset Potentially Frequent k-itemsets K th - iteration The potentially frequent itemsets are passed back to the U-Apriori algorithm to generate candidates for the next interation Patch Up Module Patch Up Module Potentially frequent itemsets Frequent itemsets in the trimmed dataset Frequent Itemsets in the original dataset The potentially frequent itemsets are verified by the patch up module against the original dataset

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Copyright 2009 by CEBT Data Trimming Framework Center for E-Business Technology Original Dataset Uncertain Apriori Uncertain Apriori Trimmed Dataset Infrequent k-itemsets Statistics Potentially Frequent k-itemsets Potentially frequent itemsets Frequent itemsets in the trimmed dataset Trimming Module Trimming Module Pruning Module Pruning Module Patch Up Module Patch Up Module Frequent Itemsets in the original dataset There are three modules under the data trimming framework, each module can have different strategies The trimming threshold is global to all items or local to each item? - Local threshold The trimming threshold is global to all items or local to each item? - Local threshold What statistics are used in the pruning strategy? -Total expected support count being trimmed of each item - Maximum existential probability being trimmed of each item What statistics are used in the pruning strategy? -Total expected support count being trimmed of each item - Maximum existential probability being trimmed of each item Can we use a single scan to verify all the potentially frequent itemsets or multiple scans over the original dataset? - Single scan Can we use a single scan to verify all the potentially frequent itemsets or multiple scans over the original dataset? - Single scan

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Copyright 2009 by CEBT Experimental Setup Center for E-Business Technology TIDItems 12,4,9 25,4,10 31,6,7 …… IBM Synthetic Datasets Generator IBM Synthetic Datasets Generator TIDItems 12(90%), 4(80%), 9(30%), 10(4%), 19(25%) 25(75%), 4(68%), 10(100%), 14(15%), 19(23%) 31(88%), 6(95%), 7(98%), 13(2%), 18(7%), 22(10%), 25(6%) …… Step 1: Generate data without uncertainty. IBM Synthetic Datasets Generator Average length of each transaction (T = 20) Average length of frequent patterns (I = 6) Number of transactions (D = 100K) Step 1: Generate data without uncertainty. IBM Synthetic Datasets Generator Average length of each transaction (T = 20) Average length of frequent patterns (I = 6) Number of transactions (D = 100K) Data Uncertainty Simulator High probability items generator High probability items generator Assign relatively high probabilities to the items in the generated dataset. Normal Distribution (mean = 95 %, standard deviation = 5%) Assign relatively high probabilities to the items in the generated dataset. Normal Distribution (mean = 95 %, standard deviation = 5%) Assign more items with relatively low p robabilities to each transaction. Normal Distribution (mean = 10%, standard deviation = 5%) Assign more items with relatively low p robabilities to each transaction. Normal Distribution (mean = 10%, standard deviation = 5%) Low probability items generator Low probability items generator Step 2 : Introduce ex istential uncertainty to each item in the gene rated dataset. The proportion of items with low pro babilities is control led by the parame ter R (R=75%).

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Copyright 2009 by CEBT CPU Cost with different R Center for E-Business Technology When R increases, more items with low existential probabilities are contained in the dataset, therefore there will be insignificant support increments Since the trimming method has avoided those insignificant support increments, the CPU cost is much smaller that the U-Apriori algorithm The trimming approach achieves positive CPU cost saving when R is over 3%. When R is too low, fewer low probability items can be trimmed and the saving cannot compensate for the extra computational cost in the patch up module The trimming approach achieves positive CPU cost saving when R is over 3%. When R is too low, fewer low probability items can be trimmed and the saving cannot compensate for the extra computational cost in the patch up module

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Copyright 2009 by CEBT Conclusion This paper discussed the problem of mining frequent itemsets from existential uncertain data Introduce the U-Apriori algorithm, which is a modified version of the Apriori algorithm, to work on such datasets Identified the computational problem of U-Apriori and proposed a data trimming framework to address this issue The Data Trimming method works well on datasets with high percentage of low probability items Center for E-Business Technology

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Copyright 2009 by CEBT Paper Evaluation Pros Well-defined Problem Good Presentation (well-organized paper) Flexible Trimming Framework My Comments Good research field & many opportunities – U-Apriori algorithm in 2007 Center for E-Business Technology

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