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  2. Cross-industry standard process for data mining - Wikipedia

    en.wikipedia.org/wiki/Cross-industry_standard...

    The outer circle in the diagram symbolizes the cyclic nature of data mining itself. A data mining process continues after a solution has been deployed. The lessons learned during the process can trigger new, often more focused business questions, and subsequent data mining processes will benefit from the experiences of previous ones.

  3. Precision and recall - Wikipedia

    en.wikipedia.org/wiki/Precision_and_recall

    In pattern recognition, information retrieval, object detection and classification (machine learning), precision and recall are performance metrics that apply to data retrieved from a collection, corpus or sample space. Precision (also called positive predictive value) is the fraction of relevant instances among the retrieved instances. Written ...

  4. 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 ...

  5. Silhouette (clustering) - Wikipedia

    en.wikipedia.org/wiki/Silhouette_(clustering)

    A plot showing silhouette scores from three types of animals from the Zoo dataset as rendered by Orange data mining suite. At the bottom of the plot, silhouette identifies dolphin and porpoise as outliers in the group of mammals. Assume the data have been clustered via any technique, such as k-medoids or k-means, into clusters.

  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. Star schema - Wikipedia

    en.wikipedia.org/wiki/Star_schema

    Fact tables record measurements or metrics for a specific event. Fact tables generally consist of numeric values, and foreign keys to dimensional data where descriptive information is kept. [4] Fact tables are designed to a low level of uniform detail (referred to as "granularity" or "grain"), meaning facts can record events at a very atomic ...

  8. Hierarchical clustering - Wikipedia

    en.wikipedia.org/wiki/Hierarchical_clustering

    Raw data. For example, suppose this data is to be clustered, and the Euclidean distance is the distance metric. The hierarchical clustering dendrogram would be: Traditional representation. Cutting the tree at a given height will give a partitioning clustering at a selected precision.

  9. 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.