I want to go through the Wikipedia series on Machine Learning and Data mining. Data mining is the process of extracting and discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems.
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. The defining characteristic of a rule-based machine learner is the identification and utilization of a set of relational rules that collectively represent the knowledge captured by the system.
Rule-based machine learning approaches include learning classifier systems, association rule learning, artificial immune systems, and any ither method that relies on a set or rules, each covering contextual knowledge.
While rule-based machine learning is conceptually a type of rule-based system, it is distinct from traditional rule-based systems, which are often hand-crafted. Rule-based machine learning applies some form of learning algorithm to automatically identify useful rules, rather than a human needing to apply prior domain knowledge to manually construct rules and create a rule set.
Rules typically take the form of an {IF:THEN} expression. An individual rule is not in itself a model, since the rule is only applicable when its condition is satisfied. Therefore, rule-based machine learning methods typically comprise a set of rules, or knowledge base, that collectively make up the prediction model.
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