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Data Classification [on-going
project]
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Classification is one of the most widely used
techniques in data mining, with a broad array of applications,
including sentiment analysis, ad targeting, spam detection, risk
assessment, medical diagnosis and image classification.
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Classification is a process of categorizing a
given set of data into classes or categories that make it easy
to retrieve, sort and store for future use.
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Classification can be performed on both
structured or unstructured data.
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The process starts with predicting the class of
given data points. The classes are often referred to as target,
label or categories.
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The classification predictive modeling is the
task of approximating the mapping function from input variables
to discrete output variables. The main goal is to identify which
class/category the new data will fall into. In other words, the
core goal of classification is to predict a category or class y
from some inputs x.
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Data classification is not the same as data
indexing, although there are some parallels between the two.
While both require looking at content to decide whether it is
relevant to a keyword or a concept, classification does not
necessarily produce a searchable index.
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click
here for further
understanding.
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