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  2. Association rule learning - Wikipedia

    en.wikipedia.org/wiki/Association_rule_learning

    Association rule learning is a rule-based machine learning method for discovering interesting relations between variables in large databases. It is intended to identify strong rules discovered in databases using some measures of interestingness. [1]

  3. Lift (data mining) - Wikipedia

    en.wikipedia.org/wiki/Lift_(data_mining)

    In data mining and association rule learning, lift is a measure of the performance of a targeting model (association rule) at predicting or classifying cases as having an enhanced response (with respect to the population as a whole), measured against a random choice targeting model.

  4. Associative classifier - Wikipedia

    en.wikipedia.org/wiki/Associative_classifier

    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".

  5. Apriori algorithm - Wikipedia

    en.wikipedia.org/wiki/Apriori_algorithm

    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.

  6. Affinity analysis - Wikipedia

    en.wikipedia.org/wiki/Affinity_analysis

    There are two important metrics for performing the association rules mining technique: support and confidence. Also, a priori algorithm is used to reduce the search space for the problem. [1] The support metric in the association rule learning algorithm is defined as the frequency of the antecedent or consequent appearing together in a data set ...

  7. Rule-based machine learning - Wikipedia

    en.wikipedia.org/wiki/Rule-based_machine_learning

    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 ...

  8. Contrast set learning - Wikipedia

    en.wikipedia.org/wiki/Contrast_set_learning

    Contrast set learning is a form of association rule learning. [2] Association rule learners typically offer rules linking attributes commonly occurring together in a training set (for instance, people who are enrolled in four-year programs and take a full course load tend to also live near campus).

  9. Data mining - Wikipedia

    en.wikipedia.org/wiki/Data_mining

    The actual data mining task is the semi-automatic or automatic analysis of large quantities of data to extract previously unknown, interesting patterns such as groups of data records (cluster analysis), unusual records (anomaly detection), and dependencies (association rule mining, sequential pattern mining).