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A computer classifier can be able to learn or can implement static classification rules. For a training data-set, the true labels y j are unknown, but it is a prime target for the classification procedure that the approximation ^ = as well as possible, where the quality of this approximation needs to be judged on the basis of the statistical or ...
That is why Association rules are typically made from rules that are well represented by the data. There are many different data mining techniques you could use to find certain analytics and results, for example, there is Classification analysis, Clustering analysis, and Regression analysis. [4]
An associative classifier (AC) is a kind of supervised learning model that uses association rules to assign a target value. The term associative classification was coined by Bing Liu et al., [1] in which the authors defined a model made of rules "whose right-hand side are restricted to the classification class attribute".
For example, there are relational classification rules (relational classification), relational regression tree, and relational association rules. There are several approaches to relational data mining: Inductive Logic Programming (ILP) Statistical Relational Learning (SRL) Graph Mining; Propositionalization; Multi-view learning
Decision tree learning is a supervised learning approach used in statistics, data mining and machine learning.In this formalism, a classification or regression decision tree is used as a predictive model to draw conclusions about a set of observations.
Data mining is the process of extracting ... and dependencies (association rule mining, ... without using known structures in the data. Classification – is the task ...
Learning classifier systems seek to identify a set of context-dependent rules that collectively store and apply knowledge in a piecewise manner in order to make predictions (e.g. behavior modeling, [3] classification, [4] [5] data mining, [5] [6] [7] regression, [8] function approximation, [9] or game strategy).
The CN2 induction algorithm is a learning algorithm for rule induction. [1] It is designed to work even when the training data is imperfect. It is based on ideas from the AQ algorithm and the ID3 algorithm. As a consequence it creates a rule set like that created by AQ but is able to handle noisy data like ID3.