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  2. Contrast set learning - Wikipedia

    en.wikipedia.org/wiki/Contrast_set_learning

    A common practice in data mining is to classify, to look at the attributes of an object or situation and make a guess at what category the observed item belongs to. As new evidence is examined (typically by feeding a training set to a learning algorithm), these guesses are refined and improved. Contrast set learning works in the opposite direction.

  3. Category:Data mining algorithms - Wikipedia

    en.wikipedia.org/wiki/Category:Data_mining...

    Classification algorithms (3 C, 85 P) Cluster analysis algorithms (42 P) Pages in category "Data mining algorithms" The following 6 pages are in this category, out of ...

  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. Decision tree learning - Wikipedia

    en.wikipedia.org/wiki/Decision_tree_learning

    Decision trees used in data mining are of two main types: Classification tree analysis is when the predicted outcome is the class (discrete) to which the data belongs. Regression tree analysis is when the predicted outcome can be considered a real number (e.g. the price of a house, or a patient's length of stay in a hospital).

  6. Tanagra (machine learning) - Wikipedia

    en.wikipedia.org/wiki/Tanagra_(machine_learning)

    Tanagra is a free suite of machine learning software for research and academic purposes developed by Ricco Rakotomalala at the Lumière University Lyon 2, France. [1] [2] Tanagra supports several standard data mining tasks such as: Visualization, Descriptive statistics, Instance selection, feature selection, feature construction, regression, factor analysis, clustering, classification and ...

  7. Data mining - Wikipedia

    en.wikipedia.org/wiki/Data_mining

    The difference between data analysis and data mining is that data analysis is used to test models and hypotheses on the dataset, e.g., analyzing the effectiveness of a marketing campaign, regardless of the amount of data. In contrast, data mining uses machine learning and statistical models to uncover clandestine or hidden patterns in a large ...

  8. Probabilistic classification - Wikipedia

    en.wikipedia.org/wiki/Probabilistic_classification

    Formally, an "ordinary" classifier is some rule, or function, that assigns to a sample x a class label ลท: y ^ = f ( x ) {\displaystyle {\hat {y}}=f(x)} The samples come from some set X (e.g., the set of all documents , or the set of all images ), while the class labels form a finite set Y defined prior to training.

  9. Massive Online Analysis - Wikipedia

    en.wikipedia.org/wiki/Massive_Online_Analysis

    Massive Online Analysis (MOA) is a free open-source software project specific for data stream mining with concept drift. It is written in Java and developed at the University of Waikato, New Zealand. [2]