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EstMax : Tracing Maximal Frequent Item Sets Instantly over Online Transactional Data Streams

Outline. AbstractIntroductionRelated workPreliminaries estMax methodExperimentsConclusions . 2. Abstract(1/2). The number of frequent item sets in a typeical data set is very large:Solution: Frequent item sets needs to be represented in a more compact notations: maximal frequent item s

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EstMax : Tracing Maximal Frequent Item Sets Instantly over Online Transactional Data Streams

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    1. estMax : Tracing Maximal Frequent Item Sets Instantly over Online Transactional Data Streams Source: IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 21, NO. 10, OCTOBER 2009 Authors: Ho Jin Woo ,Won Suk Lee Reporter: Cheng-Ting Hsieh 1

    2. Outline Abstract Introduction Related work Preliminaries estMax method Experiments Conclusions 2

    3. Abstract(1/2) The number of frequent item sets in a typeical data set is very large: Solution: Frequent item sets needs to be represented in a more compact notations: maximal frequent item set(MFI) closed frequent item set(CFI) Finding such item sets over online transactional data streams is not easy Solution: estMax method (tracing the set of MFIs instantly over an online data strean) 3

    4. Abstract(2/2) Advantages of estMax method: without any checking superset/subset mechanism extracting MFIs at any moment over online data streams 4

    5. Introduction estMax method based on estDec method the underlying node structure is prefix tree 5

    6. Related Work MOMENT Closed Enumeration Tree(CET) Direct Update tree(DIU) INSTANT Single-phase algorithm for finding MFIs over a data stream 6

    7. Preliminaries(1/5) Prefix tree: the root node has a null value each node has two fields item-id cnt n-item set e (i1,i2,…,in) nroot ?i1?i2?… ?in 7

    8. Preliminaries(2/5) detestDec method determines each item set generated in a new transaction operations of estDec method delay insertion pruning operation 8

    9. Preliminaries(3/5) delay insertion Case1: When a new 1-item appears in a new generated transaction 9

    10. Preliminaries(4/5) Case2: n-item set e(n?2) used to insignificant just becomes significant , set e is significant 10

    11. Preliminaries(5/5) pruning operation When the current support of an n-item set(n ?2) becomes less than Ssig 11

    12. estMax method(1/8) ML:Maximal lifetime , IS_MAX If the item set e is an MFI, IS_MAX = true cnt : count Ck(e) err : estimate error e(e) tid : the identifier of the least transaction that contains the item set e 12

    13. estMax method(2/8) Top-q t-max 13

    14. estMax method(3/8) Error reduction 14

    15. estMax method(4/8) estMax method: Parameter updating phase Count updating phase Item set insertion phase MFI selection phase 15

    16. estMax method(5/8) Parameter updating phase The total number of transactions in the current data stream Dk is updated 16

    17. estMax method(6/8) Count updating phase If the v.cnt < Ssig * |Dk| ? prune this node and all of it’s descendent nodes If the v.cnt ? Ssig * |Dk| , If the v.err ?Serr * |Dk | ?the item set e is a new MFI ? v.IS_MAX = True 17

    18. estMax method(7/8) Item set insertion phase (i) new 1-items (not contained in Pk-1) ? insert into Pk-1 ML=k, IS_MAX=True (ii) Filtering Tk with Ssig significant item sets (iii) Finding new significant item sets and insert into Pk-1 18

    19. estMax method(8/8) MFI selection phase retraversing the prefix tree Pk Sk(e) ?Smin and IS_MAX(e)=True then e is MFI 19

    20. Experiments(1/6) 20

    21. Experiments(2/6) 21

    22. Experiments(3/6) False negative errors False position errors 22

    23. Experiments(4/6) 23

    24. Experiments(5/6) 24

    25. Experiments(6/6) 25

    26. Conclusions By using these two parameters ML and IS_MAX tracing MFIs without superset/subset checking By several predefined thresholds diminish the false positive and negative errors Serr controlling the accuracy of MFIs top-q-Tk-maxes providing a nice trade-off between accuracy and processing time 26

    27. Thank for your attention 27

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