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

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

    Cross-industry standard process for data mining. The Cross-industry standard process for data mining, known as CRISP-DM, [1] is an open standard process model that describes common approaches used by data mining experts. It is the most widely-used analytics model. [2]

  3. Data mining - Wikipedia

    en.wikipedia.org/wiki/Data_mining

    t. e. Data mining is the process of extracting and discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. [1] Data mining is an interdisciplinary subfield of computer science and statistics with an overall goal of extracting information (with intelligent methods) from ...

  4. Process mining - Wikipedia

    en.wikipedia.org/wiki/Process_mining

    Process mining is a family of techniques used to analyze event data in order to understand and improve operational processes. Part of the fields of data science and process management, process mining is generally built on logs that contain case id, a unique identifier for a particular process instance; an activity, a description of the event that is occurring; a timestamp; and sometimes other ...

  5. SEMMA - Wikipedia

    en.wikipedia.org/wiki/SEMMA

    SEMMA. SEMMA is an acronym that stands for Sample, Explore, Modify, Model, and Assess. It is a list of sequential steps developed by SAS Institute, one of the largest producers of statistics and business intelligence software. It guides the implementation of data mining applications. [1] Although SEMMA is often considered to be a general data ...

  6. Educational data mining - Wikipedia

    en.wikipedia.org/wiki/Educational_data_mining

    Educational data mining refers to techniques, tools, and research designed for automatically extracting meaning from large repositories of data generated by or related to people's learning activities in educational settings. [4] Quite often, this data is extensive, fine-grained, and precise. For example, several learning management systems ...

  7. Predictive analytics - Wikipedia

    en.wikipedia.org/wiki/Predictive_analytics

    Predictive model solutions can be considered a type of data mining technology. The models can analyze both historical and current data and generate a model in order to predict potential future outcomes. Regardless of the methodology used, in general, the process of creating predictive models involves the same steps.

  8. Data preprocessing - Wikipedia

    en.wikipedia.org/wiki/Data_Preprocessing

    Data preprocessing. Data preprocessing can refer to manipulation, filtration or augmentation of data before it is analyzed, [1] and is often an important step in the data mining process. Data collection methods are often loosely controlled, resulting in out-of-range values, impossible data combinations, and missing values, amongst other issues.

  9. Examples of data mining - Wikipedia

    en.wikipedia.org/wiki/Examples_of_data_mining

    In business, data mining is the analysis of historical business activities, stored as static data in data warehouse databases. The goal is to reveal hidden patterns and trends. Data mining software uses advanced pattern recognition algorithms to sift through large amounts of data to assist in discovering previously unknown strategic business ...

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