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Given a data set consisting of pairs x and y, where x denotes an element of the population and y the class it belongs to, a classification rule h(x) is a function that assigns each element x to a predicted class ^ = (). A binary classification is such that the label y can take only one of two values.
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".
A famous story about association rule mining is the "beer and diaper" story. A purported survey of behavior of supermarket shoppers discovered that customers (presumably young men) who buy diapers tend also to buy beer. This anecdote became popular as an example of how unexpected association rules might be found from everyday data.
Relational data mining is the data mining technique for relational databases. [1] Unlike traditional data mining algorithms, which look for patterns in a single table (propositional patterns), relational data mining algorithms look for patterns among multiple tables (relational patterns). For most types of propositional patterns, there are ...
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.
Rule-based machine learning (RBML) is a term in computer science intended to encompass any machine learning method that identifies, learns, or evolves 'rules' to store, manipulate or apply. [ 1 ] [ 2 ] [ 3 ] The defining characteristic of a rule-based machine learner is the identification and utilization of a set of relational rules that ...
The differences between standard classification, association rule learning, and contrast set learning can be illustrated with a simple supermarket metaphor. In the following small dataset, each row is a supermarket transaction and each "1" indicates that the item was purchased (a "0" indicates that the item was not purchased):
Data mining in general and rule induction in detail are trying to create algorithms without human programming but with analyzing existing data structures. [ 1 ] : 415- In the easiest case, a rule is expressed with “if-then statements” and was created with the ID3 algorithm for decision tree learning.