Data Mining Techniques
Data mining works by using various algorithms and techniques to turn large volumes of data into useful information.
Additionally, each method is usually not related to others.
Other than the methods like decision trees and artificial neural networks discussed before, below list some of the most common ones:
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Association rules:
An association rule is a rule-based method for finding relationships between variables in a given dataset.
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Sequential pattern mining:
It finds statistically relevant patterns between data examples where the values are delivered in a sequence.
It is usually presumed that the values are discrete, and thus time series mining is closely related, but usually considered a different activity.
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K-means clustering:
It is an algorithm to partition and classify your data based on attributes or features into K number of groups.
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Similarity measurement:
The level of similarity and dissimilarity is necessary for data mining, and the data type could be multivariate (different types of measurement scale, such as nominal, ordinal, and quantitative) data and go beyond two dimensional data scale up to N dimensions.
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SVC (Support Vector Machine):
It is a supervised learning algorithm that classifies both linear and nonlinear data based on maximizing margin between support points and a nonlinear mapping to transform the original training data into a higher dimension.
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EM (Expectation-Maximization) algorithm:
It is an iterative procedure to estimate the maximum likelihood of mixture density distribution.
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Naive Bayes:
Given a set of objects, each of which belongs to a known class, and each of which has a known vector of variables, its aim is to construct a rule which will allow us to assign future objects to a class, given only the vectors of variables describing the future objects.