"The non-trivial extraction of implicit, previously unknown, and potentially useful information from data" (Frawley et al., "Knowledge discovery in databases: An overview", 1991)
"Data mining is the efficient discovery of valuable, nonobvious information from a large collection of data." (Joseph P Bigus,"Data Mining with Neural Networks: Solving business problems from application development to decision support", 1996)
"Data mining is the process of examining large amounts of aggregated data. The objective of data mining is to either predict what may happen based on trends or patterns in the data or to discover interesting correlations in the data." (Microsoft Corporation, "Microsoft SQL Server 7.0 Data Warehouse Training Kit", 2000)
"A data-driven approach to analysis and prediction by applying sophisticated techniques and algorithms to discover knowledge." (Paulraj Ponniah, "Data Warehousing Fundamentals", 2001)
"A class of undirected queries, often against the most atomic data, that seek to find unexpected patterns in the data. The most valuable results from data mining are clustering, classifying, estimating, predicting, and finding things that occur together. There are many kinds of tools that play a role in data mining. The principal tools include decision trees, neural networks, memory- and cased-based reasoning tools, visualization tools, genetic algorithms, fuzzy logic, and classical statistics. Generally, data mining is a client of the data warehouse." (Ralph Kimball & Margy Ross, "The Data Warehouse Toolkit" 2nd Ed., 2002)
"the process of extracting valid, authentic, and actionable information from large databases." (Seth Paul et al. "Preparing and Mining Data with Microsoft SQL Server 2000 and Analysis", 2002)
"Advanced analysis or data mining is the analysis of detailed data to detect patterns, behaviors, and relationships in data that were previously only partially known or at times totally unknown." (Margaret Y Chu, "Blissful Data", 2004)
"Analysis of detail data to discover relationships, patterns, or associations between values." (Margaret Y Chu, "Blissful Data ", 2004)
"An information extraction activity whose goal is to discover hidden facts contained in databases. Using a combination of machine learning, statistical analysis, modeling techniques, and database technology, data mining finds patterns and subtle relationships in data and infers rules that allow the prediction of future results." (Sharon Allen & Evan Terry, "Beginning Relational Data Modeling" 2nd Ed., 2005)
"the process of analyzing large amounts of data in search of previously undiscovered business patterns." (William H Inmon, "Building the Data Warehouse", 2005)
"A type of advanced analysis used to determine certain patterns within data. Data mining is most often associated with predictive analysis based on historical detail, and the generation of models for further analysis and query." (Jill Dyché & Evan Levy, "Customer Data Integration", 2006)
"Refers to the process of identifying nontrivial facts, patterns and relationships from large databases. The databases have often been put together for a different purpose from the data mining exercise." (Glenn J Myatt, "Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mining", 2006)
"Data mining is the process of discovering implicit patterns in data stored in data warehouse and using those patterns for business advantage such as predicting future trends." (S. Sumathi & S. Esakkirajan, "Fundamentals of Relational Database Management Systems", 2007)
"Digging through data (usually in a data warehouse or data mart) to identify interesting patterns." (Rod Stephens, "Beginning Database Design Solutions", 2008)
"Intelligently analyzing data to extract hidden trends, patterns, and information. Commonly used by statisticians, data analysts and Management Information Systems communities." (Craig F Smith & H Peter Alesso, "Thinking on the Web: Berners-Lee, Gödel and Turing", 2008)
"The process of extracting valid, authentic, and actionable information from large databases." (Darril Gibson, "MCITP SQL Server 2005 Database Developer All-in-One Exam Guide", 2008)
"The process of retrieving relevant data to make intelligent decisions." (Robert D Schneider & Darril Gibson, "Microsoft SQL Server 2008 All-in-One Desk Reference For Dummies", 2008)
"A process that minimally has four stages: (1) data preparation that may involve 'data cleaning' and even 'data transformation', (2) initial exploration of the data, (3) model building or pattern identification, and (4) deployment, which means subjecting new data to the 'model' to predict outcomes of cases found in the new data." (Robert Nisbet et al, "Handbook of statistical analysis and data mining applications", 2009)
"Automatically searching large volumes of data for patterns or associations." (Mark Olive, "SHARE: A European Healthgrid Roadmap", 2009)
"The use of machine learning algorithms to find faint patterns of relationship between data elements in large, noisy, and messy data sets, which can lead to actions to increase benefit in some form (diagnosis, profit, detection, etc.)." (Robert Nisbet et al, "Handbook of statistical analysis and data mining applications", 2009)
"A data-driven approach to analysis and prediction by applying sophisticated techniques and algorithms to discover knowledge." (Paulraj Ponniah, "Data Warehousing Fundamentals for IT Professionals", 2010)
"A way of extracting knowledge from a database by searching for correlations in the data and presenting promising hypotheses to the user for analysis and consideration." (Toby J Teorey, "Database Modeling and Design" 4th Ed., 2010)
"The process of using mathematical algorithms (usually implemented in computer software) to attempt to transform raw data into information that is not otherwise visible (for example, creating a query to forecast sales for the future based on sales from the past)." (Ken Withee, "Microsoft Business Intelligence For Dummies", 2010)
"A process that employs automated tools to analyze data in a data warehouse and other sources and to proactively identify possible relationships and anomalies." (Carlos Coronel et al, "Database Systems: Design, Implementation, and Management" 9th Ed., 2011)
"Process of analyzing data from different perspectives and summarizing it into useful information (e.g., information that can be used to increase revenue, cuts costs, or both)." (Linda Volonino & Efraim Turban, "Information Technology for Management" 8th Ed., 2011)
"The process of sifting through large amounts of data using pattern recognition, fuzzy logic, and other knowledge discovery statistical techniques to identify previously unknown, unsuspected, and potentially meaningful data content relationships and trends." (DAMA International, "The DAMA Dictionary of Data Management", 2011)
"Data mining, a branch of computer science, is the process of extracting patterns from large data sets by combining statistical analysis and artificial intelligence with database management. Data mining is seen as an increasingly important tool by modern business to transform data into business intelligence giving an informational advantage." (T T Wong & Loretta K W Sze, "A Neuro-Fuzzy Partner Selection System for Business Social Networks", 2012)
"Field of analytics with structured data. The model inference process minimally has four stages: data preparation, involving data cleaning, transformation and selection; initial exploration of the data; model building or pattern identification; and deployment, putting new data through the model to obtain their predicted outcomes." (Gary Miner et al, "Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications", 2012)
"The process of identifying commercially useful patterns or relationships in databases or other computer repositories through the use of advanced statistical tools." (Microsoft, "SQL Server 2012 Glossary", 2012)
"The process of exploring and analyzing large amounts of data to find patterns." (Marcia Kaufman et al, "Big Data For Dummies", 2013)
"An umbrella term for analytic techniques that facilitate fast pattern discovery and model building, particularly with large datasets." (Meta S Brown, "Data Mining For Dummies", 2014)
"Analysis of large quantities of data to find patterns such as groups of records, unusual records, and dependencies" (Daniel Linstedt & W H Inmon, "Data Architecture: A Primer for the Data Scientist", 2014)
"The practice of analyzing big data using mathematical models to develop insights, usually including machine learning algorithms as opposed to statistical methods."(Brenda L Dietrich et al, "Analytics Across the Enterprise", 2014)
"Data mining is the analysis of data for relationships that have not previously been discovered." (Piyush K Shukla & Madhuvan Dixit, "Big Data: An Emerging Field of Data Engineering", Handbook of Research on Security Considerations in Cloud Computing, 2015)
"A methodology used by organizations to better understand their customers, products, markets, or any other phase of the business." (Adam Gordon, "Official (ISC)2 Guide to the CISSP CBK" 4th Ed., 2015)
"Extracting information from a database to zero in on certain facts or summarize a large amount of data." (Faithe Wempen, "Computing Fundamentals: Introduction to Computers", 2015)
"It refers to the process of identifying and extracting patterns in large data sets based on artificial intelligence, machine learning, and statistical techniques." (Hamid R Arabnia et al, "Application of Big Data for National Security", 2015)
"The process of exploring and analyzing large amounts of data to find patterns." (Judith S Hurwitz, "Cognitive Computing and Big Data Analytics", 2015)
"Term used to describe analyzing large amounts of data to find patterns, correlations, and similarities." (Brittany Bullard, "Style and Statistics", 2016)
"The process of extracting meaningful knowledge from large volumes of data contained in data warehouses." (K N Krishnaswamy et al, "Management Research Methodology: Integration of Principles, Methods and Techniques", 2016)
"A class of analytical applications that help users search for hidden patterns in a data set. Data mining is a process of analyzing large amounts of data to identify data–content relationships. Data mining is one tool used in decision support special studies. This process is also known as data surfing or knowledge discovery." (Daniel J Power & Ciara Heavin, "Decision Support, Analytics, and Business Intelligence" 3rd Ed., 2017)
"The process of collecting, searching through, and analyzing a large amount of data in a database to discover patterns or relationships." (Jonathan Ferrar et al, "The Power of People: Learn How Successful Organizations Use Workforce Analytics To Improve Business Performance", 2017)
"Data mining involves finding meaningful patterns and deriving insights from large data sets. It is closely related to analytics. Data mining uses statistics, machine learning, and artificial intelligence techniques to derive meaningful patterns." (Amar Sahay, "Business Analytics" Vol. I, 2018)
"The analysis of the data held in data warehouses in order to produce new and useful information." (Shon Harris & Fernando Maymi, "CISSP All-in-One Exam Guide" 8th Ed., 2018)
"The process of collecting critical business information from a data source, correlating the information, and uncovering associations, patterns, and trends." (Sybase, "Open Server Server-Library/C Reference Manual", 2019)
"The process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems." (Dmitry Korzun et al, "Semantic Methods for Data Mining in Smart Spaces", 2019)
"A technique using software tools geared for the user who typically does not know exactly what he's searching for, but is looking for particular patterns or trends. Data mining is the process of sifting through large amounts of data to produce data content relationships. It can predict future trends and behaviors, allowing businesses to make proactive, knowledge-driven decisions. This is also known as data surfing." (Information Management)
"An analytical process that attempts to find correlations or patterns in large data sets for the purpose of data or knowledge discovery." (NIST SP 800-53)
"Extracting previously unknown information from databases and using that data for important business decisions, in many cases helping to create new insights." (Solutions Review)
"is the process of collecting data, aggregating it according to type and sorting through it to identify patterns and predict future trends." (Accenture)
"the process of analyzing large batches of data to find patterns and instances of statistical significance. By utilizing software to look for patterns in large batches of data, businesses can learn more about their customers and develop more effective strategies for acquisition, as well as increase sales and decrease overall costs." (Insight Software)
"The process of identifying commercially useful patterns or relationships in databases or other computer repositories through the use of advanced statistical tools." (Microsoft)
"The process of pulling actionable insight out of a set of data and putting it to good use. This includes everything from cleaning and organizing the data; to analyzing it to find meaningful patterns and connections; to communicating those connections in a way that helps decision-makers improve their product or organization." (KDnuggets)
"Data mining is the process of analyzing hidden patterns of data according to different perspectives for categorization into useful information, which is collected and assembled in common areas, such as data warehouses, for efficient analysis, data mining algorithms, facilitating business decision making and other information requirements to ultimately cut costs and increase revenue. Data mining is also known as data discovery and knowledge discovery." (Techopedia)
"Data mining is an automated analytical method that lets companies extract usable information from massive sets of raw data. Data mining combines several branches of computer science and analytics, relying on intelligent methods to uncover patterns and insights in large sets of information." (Sisense) [source]
"Data mining is the process of analyzing data from different sources and summarizing it into relevant information that can be used to help increase revenue and decrease costs. Its primary purpose is to find correlations or patterns among dozens of fields in large databases." (Logi Analytics) [source]
"Data mining is the process of analyzing massive volumes of data to discover business intelligence that helps companies solve problems, mitigate risks, and seize new opportunities." (Talend) [source]
"Data Mining is the process of collecting data, aggregating it according to type and sorting through it to identify patterns and predict future trends." (Accenture)
"Data mining is the process of discovering meaningful correlations, patterns and trends by sifting through large amounts of data stored in repositories. Data mining employs pattern recognition technologies, as well as statistical and mathematical techniques." (Gartner)
"Data mining is the process of extracting relevant patterns, deviations and relationships within large data sets to predict outcomes and glean insights. Through it, companies convert big data into actionable information, relying upon statistical analysis, machine learning and computer science." (snowflake) [source]
"Data mining is the work of analyzing business information in order to discover patterns and create predictive models that can validate new business insights. […] Unlike data analytics, in which discovery goals are often not known or well defined at the outset, data mining efforts are usually driven by a specific absence of information that can’t be satisfied through standard data queries or reports. Data mining yields information from which predictive models can be derived and then tested, leading to a greater understanding of the marketplace." (Informatica) [source]
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