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Anomaly Detection

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Running head: ANOMALY DETECTION 1
Anomaly Detection
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ANOMALY DETECTION
2
Anomaly
An Anomaly can be defined as an inconsistency. It is a variation from the norm, a blip on
the screen of life that doesn't fit with whatever remains of the example. When something is
unordinary contrasted with similar things around it, it's the anomaly (Agrawal & Agrawal, 2015).
The definition that an anomaly is an object that is unusually influential in the creation of a data
model is true to some extent. Anomalies are usually considered not fit; therefore they cannot be
used in influencing the production of other new models. However it is not just about creating
new models, an anomaly is an object that is different from the standard set object, whether it can
be used in influencing the creation of other objects or not (Iturbe, Garitano, Zurutuza &
Uribeetxeberria, 2017). Not every object that is less influential in creating other models is an
anomaly. Some objects are considered less prominent because of other reasons even when they
have no defect to make then an anomaly. It is an object perceived not fit or not up to standard. It
is a deviation from the typical or regular request or shape or principle. This means that
determining an anomaly is based on certain set standards or a particular order and whatever does
not fall along that category is considered a defect. Therefore an object being unusually influential
in the creation of a data model is usually as a result of the object being an anomaly but defining
an anomaly cannot be based on such a definition (Iturbe, Garitano, Zurutuza & Uribeetxeberria,
2017)
This definition is appropriate for medium data. This is because it is right to the extent that
anomalies are usually not used in the creation of a data model since they are considered to have
deviated from the standard order making them not normal (Agrawal & Agrawal, 2015).
However, this definition is not appropriate for large sets of data since there may be other
underlying factors that have made an object not be used to influence the creation of different sets

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Running head: ANOMALY DETECTION 1 Anomaly Detection Student Name Course/Number Due Date Faculty Name ANOMALY DETECTION 2 Anomaly An Anomaly can be defined as an inconsistency. It is a variation from the norm, a blip on the screen of life that doesn't fit with whatever remains of the example. When something is unordinary contrasted with similar things around it, it's the anomaly (Agrawal & Agrawal, 2015). The definition that an anomaly is an object that is unusually influential in the creation of a data model is true to some extent. Anomalies are usually considered not fit; therefore they cannot be used in influencing the production of other new models. However it is not just about creating new models, an anomaly is an object that is different from the standard set object, whether it can be used in influencing the creation of other objects or not (Iturbe, Garitano, Zurutuza & Uribeetxeberria, 2017). Not every object that is less influential in creating other models is an anomaly. Some objects are considered less prominent because of other reasons even when they have no defect to make then an anomaly. It is an object perceived not fit or not up to standard. It is a deviation fro ...
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