Mining Relationships Among Interval-based Events for ClassificationDhaval PatelWynne Hsu MongLi Lee

SIGMOD 08

Outline.IntroductionPreliminariesAugment hierarchical representationInterval-based event miningInterval-based event classifierExperimentConclusion

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

Introduction.(cont)

Training data

Classification algorithm

Classificationmodel

Input the questions

The answer

Introduction.(cont)

Sheet1

ageincomestudentcredit_ratingbuys_computer

40lowyesfairyes

>40lowyesexcellentno

3140lowyesexcellentyes

Introduction.(cont)

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 }

Augment hierarchical representation.

Before

Meet

Overlap

Start

Finish

Contain

Equal

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

Augment hierarchical representation(cont.)

Augment hierarchical representation(cont.)The linear ordering of

is {{A+}{B+}{C+}{A}{B}{D+}{D}{C}}

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.

Interval-based event mining(cont.)

Interval-based event mining(cont.)Support count

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.

IEClassifier.(cont)Let PatternMatchI be the set of discriminating patterns that are contained in I

Experiment.

Experiment.(cont)Nearest Neighbor(Neural Networks)Decision TreeSVM Hyper-planHyper-plan

Conclusion.IEMiner algorithm

IEClassification

The performance improved

It achieved the best accuracy