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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".
C4.5 is an algorithm used to generate a decision tree developed by Ross Quinlan. [1] C4.5 is an extension of Quinlan's earlier ID3 algorithm.The decision trees generated by C4.5 can be used for classification, and for this reason, C4.5 is often referred to as a statistical classifier.
Decision tree learning is a supervised learning approach used in statistics, data mining and machine learning.In this formalism, a classification or regression decision tree is used as a predictive model to draw conclusions about a set of observations.
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 ...
Download as PDF; Printable version; ... This category is about statistical classification algorithms. ... Analogical modeling;
In some of these, it is employed as a data mining procedure, while in others more detailed statistical modeling is undertaken. Biological classification – The science of identifying, describing, defining and naming groups of biological organisms
Oracle Data Mining (ODM) is an option of Oracle Database Enterprise Edition. It contains several data mining and data analysis algorithms for classification, prediction, regression, associations, feature selection, anomaly detection, feature extraction, and specialized analytics.
Abstractly, naive Bayes is a conditional probability model: it assigns probabilities (, …,) for each of the K possible outcomes or classes given a problem instance to be classified, represented by a vector = (, …,) encoding some n features (independent variables).