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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".
Weighted class learning is another form of associative learning where weights may be assigned to classes to give focus to a particular issue of concern for the consumer of the data mining results. High-order pattern discovery facilitates the capture of high-order (polythetic) patterns or event associations that are intrinsic to complex real ...
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.
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.
For more information, see Statistical classification. Subcategories. ... Group method of data handling; H. Hierarchical classification; Hyper basis function network; I.
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.
This data mining method has been explored in different fields including disease diagnosis, market basket analysis, retail industry, higher education, and financial analysis. In retail, affinity analysis is used to perform market basket analysis, in which retailers seek to understand the purchase behavior of customers.
This corrects the Bias of the neural network ensemble. An associative neural network has a memory that can coincide with the training set. If new data become available, the network instantly improves its predictive ability and provides data approximation (self-learns) without retraining.