Data mining Definitions

  • A

    ad hoc analysis

    Ad hoc analysis is the term commonly used in businesses to describe a product (analytical report, statistical analysis or model, or other report or summary of data) produced one time to answer a single, specific business question. 

  • advanced analytics

    While the traditional analytical tools that comprise basic business intelligence (BI) examine historical data, tools for advanced analytics focus on forecasting future events and behaviors

  • association rules (in data mining)

    Association rules are if/then statements that help uncover relationships between seemingly unrelated data in a transactional database, relational database or other information repository.  

  • B

    backpropagation algorithm

    Backpropagation (backward propagation) is an important mathematical tool for improving the accuracy of predictions in data mining and machine learning. Essentially, backpropagation is an algorithm used to calculate derivatives quickly.

  • big data analytics

    Big data analytics is the process of examining large and varied data sets -- i.e., big data -- to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful information that can help organizations make more-informed business decisions.

  • D

    data artist

    A data artist is a business analytics (BA) specialist who creates graphs, charts, infographics and other visual tools that help people understand complex data.

  • data exploration

    Data exploration should be the first step in any data analysis. Its purpose is to familiarize analysts with the features of the dataset with which they are working.

  • data science as a service (DSaaS)

    Data science as a service (DSaaS) is the delivery of analytical reports and data products with embedded analytics produced by a service provider's team of data scientists to a client enterprise.

  • data scientist

    A data scientist is a professional responsible for collecting, analyzing and interpreting large amounts of data to identify ways to help a business improve operations and gain a competitive edge over rivals.

  • deep analytics

    Deep analytics is the application of sophisticated data processing techniques to yield information from large and typically multi-source data sets comprised of both unstructured and semi-structured data.

  • E

    ensemble modeling

    Ensemble modeling is the process of running two or more related but different analytical models and then synthesizing the results into a single score or spread in order to improve the accuracy of predictive analytics and data mining applications.

  • I

    in-memory analytics

    In-memory analytics queries data residing in a computer’s random access memory (RAM) rather than data stored on physical disks. This results in vastly shortened query response times.

  • N

    noisy data

    Noisy data is meaningless data. The term was often used as a synonym for corrupt data, but its meaning has expanded to include data from unstructured text that cannot be understood by machines.  

  • O

    opinion mining (sentiment mining)

    Opinion mining is a process for tracking the mood of the public about a certain product, for example, by building a system to examine the conversations happening around it.

  • S

    social analysis

    Social analysis is the practice of analyzing a situation or social problem through objective, systematic exploration.

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