predix transform 2016 - catching outliers with cluster analysis
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PAN3: Catching Outliers with Cluster AnalysisRobin Louvet, GE Energy [email protected], @rlouvet
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AgendaPatterns in Time Series1Catching OutliersCluster AnalysisPredix Analytics
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Patterns in Time SeriesTime Series is a predominant raw data type in industry
Signal Processing Independent Single Samples
Machine Learning Huge Volume of Historical Samples
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Catching OutliersSpotting abnormal patterns can be critical in industry:
• Fraudulent Transaction Blocking• Asset Health Monitoring• Non-technical Losses On Networks
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Cluster Analysis
(source: Wikipedia, Cluster Analysis)
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Cluster Analysis
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Predix Analytics
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Predix Analytics{ “ntl-detection-py” : {
"tags": { "analytic-root": "analytic", "driver-root": "driver", "driver-main": "driver/AnalyticDriver.py",
"mapper": "driver", "resultprovider": "getOutput" } },
"libs": [ "boto3" ], "conda-libs": [ "numpy", "scipy", "pandas",
"scikit-learn" ] }
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Predix Analytics (demo)
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