This page (revision-31) was last changed on 13-Sep-2022 16:14 by Dan Hudson

This page was created on 22-Mar-2022 15:18 by Dan Hudson

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Page References

Incoming links Outgoing links
Exploratory Pattern Analytics
Exploratory Pattern Analytics

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Difference between version and

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\\__%%( color: #003399; font-size: 16px;)Required skills: __\\
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An
important step in data analysis is data exploration, to achieve a
better understanding of the data. The ''Exploratory
Pattern Analytics (EPA)'' tool works on
prepared/preprocessed tabular data. It provides explanatory patterns,
i.e., simple rules between some parameters (e.g., temperature,
pressure) that are predictive for a certain target parameter (e.g.,
scrap rate). This provides important insights enhancing data
understanding.
\\__%%( color: #003399; font-size: 16px;)Required skills: __
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\\The EPA tool is integrated into the Data Analytics tool which requires the following programs:
The EPA tool is integrated into the Data Analytics tool which requires the following programs:
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- (Text)
Explore your dataset, by finding interesting groups of data points within your data, which are described through simple patterns.
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(Text)
For
example, the EPA tool could be used to better understand why certain known
outliers occur in process data. The Exploratory Pattern Analytics
tool makes it possible to find patterns that explain the data, in the
form of rules that associate a certain combination of properties to a
high probability of an outlier occurring. This can be an exploratory
process, using input from business experts to refine the patterns.
Ultimately, the rules found by the Exploratory Pattern Analytics tool
make it possible to identify conditions under which an anomaly is
more likely, making prediction easier and offering possible causal
explanations – so that the anomalous behavior can be managed more
effectively.
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\\__%%( color: #003399; font-size: 18x;)Example screenshots:__
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This page (revision-31) was last changed on 13-Sep-2022 16:14 by Dan HudsonTop