3 Introduction. Predicts categorical class labels Classifies
data (constructs a model) based on the training set and the values
(class labels) in a classifying attribute and uses it in
classifying new data A Two-Step Process Model construction Model
usage
7 Preliminaries. E = (type, start, end) EL = {E 1, E 2, .., E n
} The length of EL, given by |EL| is the number of events in the
list. Composite event E = (E i R E j ) The start time of E is given
by min{ E i.start, E j.start } end time is max{E i.end, E j.end
}
9 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] C C =
contain count F = nish by count M = meet count O=overlap count S =
start count
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10 Augment hierarchical representation (cont.)
Slide 11
11 Augment hierarchical representation (cont.) The linear
ordering of is {{A+}{B+}{C+}{A}{B}{D+}{D}{C}}
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12 Interval-based event mining. Candidate generation Theorem. 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 event Dominant 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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13 Interval-based event mining (cont.)
Slide 14
14 Interval-based event mining (cont.) Support count
Slide 15
15 IEClassifier. Class labels C i 1 i c, c is the number of
class label The 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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16 IEClassifier. (cont) Let PatternMatch I be the set of
discriminating patterns that are contained in I
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17 Experiment.
Slide 18
18 Experiment. (cont) Nearest Neighbor (Neural Networks)
Decision Tree SVM Hyper-plan Hyper- plan
Slide 19
19 Conclusion. IEMiner algorithm IEClassification The
performance improved It achieved the best accuracy