Download Parallel Monte-Carlo Tree Search

Amol Ghoting, Srinivasan Parthasarathy, Matthew Eric Otey
Data Mining and Knowledge Discovery Vol. 16 No. 3, 2008
Reporter : CHENG-WEI, CHOU
Jan. 13 2010
組員名單：
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陳永洲
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Introduction
Distance-based outlier detection
Outlier detection algorithm
Experiment results
Conclusion
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Introduction
Distance-based outlier detection
Outlier detection algorithm
Experiment results
Conclusion
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A common problem : automatically finding outliers
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Outliers : those points are highly unlikely to occur
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A measure of unusualness : a point’s distance
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On high-dimensional, existing algorithms have not
good performance
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This paper further improve the scaling behavior of
distance-based outlier detection on large,
high-dimensional datasets
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2017/5/5
Introduction
Distance-based outlier detection
Outlier detection algorithm
Experiment results
Conclusion
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Three popular definitions of distance-based outliers:
Outliers are the data points for which there are
fewer than p other data points within distance d
Outliers are the top n data points whose distance to
their kth nearest neighbor is greatest
Outliers are the top n data points whose average
distance to their k nearest neighbors is greatest
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NL(nested loop) algorithm : the best performance in
high-dimensional spaces
For each data point in D, scan the dataset and keep
track of its k closest neighbors
Maintain a cutoff threshold, c
If (distance of a data point’s kth closest neighbor < c)
the data point is no longer an outlier
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2017/5/5
Introduction
Distance-based outlier detection
Outlier detection algorithm
Experiment results
Conclusion
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RBRP(Recursive Bining and Re-Projection)
A two-phase algorithm for fast mining of distancebased outliers in high dimensional datasets
Finds the top n outliers in the dataset whose
distance to their kth nearest neighbor is the
greatest
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First phase of RBRP
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Goal : to partition the dataset into bins
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Points that are close to each other in space are
likely to be assigned to the same bin
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A recursive procedure similar to divisive
hierarchical clustering
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Second phase of RBRP : Use an extension of the NL
algorithm to find outliers in the dataset
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Time Complexity of Phase 1 :
T ( N )  T ( N  m)  T (m)   ( N )
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Worst case :
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Best case :
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Average case:
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Introduction
Distance-based outlier detection
Outlier detection algorithm
Experiment results
Conclusion
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2017/5/5
Introduction
Distance-based outlier detection
Outlier detection algorithm
Experiment results
Conclusion
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Presented RBRP
RBRP improves upon the scaling behavior of the
state-of-the-art
Provide theoretical arguments
Validated its scaling behavior
Empirical results on real data back the above claim
Realizing a significant speedup over ORCA
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Thank you!!
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