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RULES family algorithms are mainly used in data mining to create a model that predicts the actions of a given input features. It goes under the umbrella of inductive learning, which is a machine learning approach.
Quantitative Association Rules categorical and quantitative data Interval Data Association Rules e.g. partition the age into 5-year-increment ranged Sequential pattern mining discovers subsequences that are common to more than minsup (minimum support threshold) sequences in a sequence database, where minsup is set by the user. A sequence is an ...
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 ...
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.
Apriori [1] is an algorithm for frequent item set mining and association rule learning over relational databases. It proceeds by identifying the frequent individual items in the database and extending them to larger and larger item sets as long as those item sets appear sufficiently often in the database.
The Rete algorithm (/ ˈ r iː t iː / REE-tee, / ˈ r eɪ t iː / RAY-tee, rarely / ˈ r iː t / REET, / r ɛ ˈ t eɪ / reh-TAY) is a pattern matching algorithm for implementing rule-based systems. The algorithm was developed to efficiently apply many rules or patterns to many objects, or facts , in a knowledge base .
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.
A classic example of a production rule-based system is the domain-specific expert system that uses rules to make deductions or choices. [1] For example, an expert system might help a doctor choose the correct diagnosis based on a cluster of symptoms, or select tactical moves to play a game.