mining relationships among interval-based events for classification
DESCRIPTION
Mining Relationships Among Interval-based Events for Classification. Dhaval Patel 、 Wynne Hsu Mong 、 Li Lee SIGMOD 08. Outline. Introduction Preliminaries Augment hierarchical representation Interval-based event mining Interval-based event classifier Experiment Conclusion. - PowerPoint PPT PresentationTRANSCRIPT
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Mining Relationships Among Interval-based Events for ClassificationDhaval PatelWynne Hsu MongLi Lee
SIGMOD 08
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Outline.IntroductionPreliminariesAugment hierarchical representationInterval-based event miningInterval-based event classifierExperimentConclusion
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Introduction.Predicts categorical class labelsClassifies data (constructs a model) based on the training set and the values (class labels) in a classifying attribute and uses it in classifying new dataA Two-Step Process Model construction Model usage
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Introduction.(cont)
Training data
Classification algorithm
Classificationmodel
Input the questions
The answer
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Introduction.(cont)
Sheet1
ageincomestudentcredit_ratingbuys_computer
40lowyesfairyes
>40lowyesexcellentno
3140lowyesexcellentyes
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Introduction.(cont)
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Preliminaries.E = (type, start, end)EL = {E1, E2,.., En}The length of EL, given by |EL| is the number of events in the list.Composite event E = (Ei R Ej)The start time of E is given by min{ Ei.start, Ej.start }end time is max{Ei.end, Ej.end }
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Augment hierarchical representation.
Before
Meet
Overlap
Start
Finish
Contain
Equal
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Augment hierarchical representation(cont.)((A overlap B) overlap C)1.2.
(A Overlap[0,0,0,1,0] B) Overlap[0,0,0,1,0] CC = contain countF = nish by count M = meet countO=overlap count S = start count
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Augment hierarchical representation(cont.)
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Augment hierarchical representation(cont.)The linear ordering of
is {{A+}{B+}{C+}{A}{B}{D+}{D}{C}}
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Interval-based event mining.Candidate generationTheorem.A (k+1)-pattern is a candidate pattern if it is generated from a frequent k-pattern and a 2-pattern where the 2-pattern occurs in at least k 1 frequent k-patterns.
Dominant eventDominant event in the pattern P if it occurs in P and has the latest end time among all the events in P.
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Interval-based event mining(cont.)
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Interval-based event mining(cont.)Support count
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IEClassifier.Class labels Ci 1i c, c is the number of class labelThe information gain:
p(TP) is probability of pattern TP to occur in datasets.Whose information gain values are below a predefined info_gain threshold are removed.
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IEClassifier.(cont)Let PatternMatchI be the set of discriminating patterns that are contained in I
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Experiment.
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Experiment.(cont)Nearest Neighbor(Neural Networks)Decision TreeSVM Hyper-planHyper-plan
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Conclusion.IEMiner algorithm
IEClassification
The performance improved
It achieved the best accuracy