The act of analyzing a database or data warehouse and searching for new facts based on the data. For example a supermarket may mine its customer data and...
A process of searching data bases for unique trends or occurring situations and displaying those trends to the user.
Finding unexpected relationships in a data set. Similar to exploratory data analysis. Vitalnet is excellent at data mining. Of course, keep in mind that the more you look, the more unusual events you will find, just by chance.
the process of analyzing data in a data warehouse by using special tools to find information useful for business decisions that might otherwise not be discovered.
The process of using database applications to look for hidden patterns that reveal valid and potentially useful information in different groups of data. For example, a company may engage in this process to search for buying patterns in their customer base. ata Warehouses See Data Warehousing.
A set of computer-based techniques for extracting useful meanings from huge databases.
The automated or semi-automated search for relationships and global patterning within data. Data mining techniques include data visualization, neural network analysis, and genetic algorithms.
The extraction of patterns and other useful information from a corpus of data.
The ability to query very large databases in order to satisfy a hypothesis ("top-down" data mining); or to interrogate a database in order to generate new hypotheses based on rigorous statistical correlations ("bottom-up" data mining).
uses statistical software to organize raw data into information that is useful for managerial decisions, (p. 149)
A category of software products that describe the ability to scan a data pool for common factors, organizing the data to reveal a pattern. Since over 80% of the very information a company needs on its competition and on its market is already contained within the company, a data mining product potentially can offer a powerful intelligence tool to certain companies, such as credit card firms, large retailers or distributors, who already own large data pools on customer activity.
The process of thumbing around in your data warehouse to find correlations and trends that you can use to sell more customers more stuff.
A technique for detecting relationships within the data that can be exploited for commercial advantage.
the scanning of large amounts of data in order to extract information e.g. statistical information on ATM usage.
The analysis of extremely large, and often ignored or undervalued, data that has been collected as a result of the normal progression of business issues, such as sales figures, mailing lists, and product complaint information.
An analytical technique that involves the analysis of large databases, such as data warehouses, to identify possible trends and problems.
Software that scans large amounts of data stored in data warehouses to reveal patterns or correlations. Demographic or behavioral information about people is often revealed through data mining.
automated information retrieval using a computer facilitated process. It's most often associated with the use of a model to produce patterns for predicting something, such as customer sales transactions or demographics see profiling and electronicall. Data mining is a part of searching the Web based on ( quote), when you perform a keyword search using an engine, that doesn't scan the entire Web right then and there to find results. Instead, utilities called crawlers (also known as spiders or bots) regularly seek information from a variety of sources, including popular webpages and servers containing regularly accessed URLs ...* definition of data mining defined definition of crawler defined definition of webcrawler defined What is data mining
A highly automated process that compares the consensus sequence for each identified gene to all known genes and motifs; results are stored in an annotated database. Extraction of previously unknown and potentially useful information from biological data, such as the search of gene and protein databases for relationships and global patterns associated with structure and function.
Systematic search for personal data using customer profiles. Found data gets utilized during telephone sales. Similar to selection: search through data using certain marketing criteria.
A group of database applications that seek hidden patterns in a group of data to predict future behaviour. True data mining software discovers previously unknown relationships among the data as well as changing the presentation. The software is popular in science and mathematical fields, however it is also used by marketers to comprehend consumer data.
The process of analyzing large amounts of data in order to extract new kinds of useful information (such as implicit relationships between different pieces of information).
The detection of trends and associations in a set of customer data.
Class of database applications that look for hidden patterns in a group of data. For example, data mining software can help retail companies find customers with common interests.
An information extraction activity. Data mining goal is to discover new knowledge, revealing hidden facts contained in databases. Some Data mining methods are: statistical analysis, neural networks, machine learning, modeling, database technologies. The result of Data mining processing are often the decision rules, used to predict future results.
The process of extracting previously unknown, valid and actionable information or relationships from large databases and using that information to make important decisions.
The extraction of nontrivial, implicit and previously unknown combinations of information from large amounts of data using computer software. It is a discovery process that allows marketing professionals to understand data relationships for the identification and segmentation of valuable customers in order to increase revenue from existing business.
Discovery mode of data analysis, or analyzing detail data to unearth unsuspected or unknown relationships, patterns and associations that might be of value to the organization. Advanced analysis used to determine certain patterns within data. Most often associated with predictive analysis. A process of analyzing large amounts of data to identify patterns, trends, activities, and content of data content relationships.
data processing using sophisticated data search capabilities and statistical algorithms to discover patterns and correlations in large preexisting databases; a way to discover new meaning in data
The analysis of data for relationships that have not previously been discovered.
As the term suggests, data mining is the analysis of data to establish relationships and identify patterns.
The process of extracting meaningful information (such as performance trends) from a large data set or data warehouse. PureShare eliminates the need for complex, labor-intensive data mining tasks by making it simple to create custom dashboards and reports via the web. PureShare ActiveMetrics and ReportRouter applications automate complex data mining so that users can spend their time acting on the information that PureShare reveals.
The term data mining is somewhat overloaded. It sometimes refers to the whole process of knowledge discovery and sometimes to the specific machine learning phase.
use of software tools to query information in a data warehouse
range of techniques for identifying, connecting and using data, especially that held in a data warehouse
Analysis of vast quantities of data to find precious information about customers, suppliers etc. Involves elementary analysis and multi-point statistical and temporal analysis.
A term used to describe the practice of analizing historical data in order to find patterns that will lead to increased sales, improved efficiency, etc.
the analysis and interpretation of data to establish patterns or associations within it using statistics or artificial intelligence. Techniques including clustering data sets, predictive modelling and time series analysis
A technique using computer software designed to make it easy to search large amounts of data. It looks for general patterns or trends to filter the data based on search criteria.
Analyzing information to identify trends, patterns and business opportunities.
Software tools that allow users to examine large volumes of numerical data to discover hidden patterns and cross ‑correlations that would be difficult or impossible to establish using normal query and analysis techniques. They use a number of advanced mathematical algorithms and applications including customer segmentation, shopping basket analysis, promotion effectiveness, customer vulnerability analysis, cross‑selling, portfolio creation and fraud detection.
A process of analyzing large amounts of data to identify hidden relationships, patterns, and relationships. This is often called "discovery-driven' data analysis.
A technique using software tools geared for the user who typically does not know exactly what to search for, but is looking for particular patterns or trends in large amounts of data. Data mining is the process of sifting through large amounts of data to produce data content relationships. DAMA web site at www.dmreview.com
The process of using statistical techniques to discover subtle relationships between data items and the construction of models based on them.
The practice of searching databases for hidden patterns of data which reveal additional information to create detailed profiles -- which may or may not be sold to third-parties.
The practice of extracting data from a data warehouse in order to analyze patterns, trends and relationships.
A technique for analysing data in very large databases and making new connections between the data in order to reveal trends and patterns.
Extracting knowledge from information. Data mining is also referred to as data warehousing or database mining because the information is extrapolated out of a central database or through a program that can pull bits of data from various independent databases.
The process of identifying patterns from typically large amounts of business data and extracting useful information. It can be performed by people, intelligent agents, or other machine-based learning and analysis techniques. Data mining is often applied to data stored on a data warehouse.
Using advanced statistical tools to identify commercially useful patterns in databases.
The process of collecting and aggregating data from sources such as polls and surveys. Quite of this can prove to be valuable statistical data for a companies marketing department
The process of using intelligent software to analyze data warehouses for patterns and relationships.
The process of finding hidden patterns and relationships in the data. Using a com-bination of machine learning, statistical analysis modelling techniques and database technology, data mining finds patterns and sublte relationships in data and infers rules that allow the prediction of future results. Typical applications include market segmentation, customer profiling, evaluation of retail promotions, and credit risk analysis.
Data mining is the process of analysing business data in a data warehouse to find unknown patterns or rules of information that you can use to tailor business operations. Data mining can find patterns in data to answer questions, such as what item purchased in a given transaction triggers the purchase of additional related items or what items tend to be purchased using credit cards, cash or cheques. Data Mining predicts future trends and behaviours, allowing businesses to make proactive, knowledge-driven decisions.
Sorting through data to identify patterns and establish relationships. Data mining parameters include: Association - looking for patterns where one event is connected to another event. Sequence or path analysis - looking for patterns where one event leads to another later event. Classification - looking for new patterns (May result in a change in the way the data is organized but that's ok). Clustering - finding and visually documenting groups of facts not previously known. Forecasting - discovering patterns in data that can lead to reasonable predictions about the future.
The process of finding relevant patterns, trends and relationships in a data warehouse. Data mining software tools are able to find such patterns in large amounts of data.
Data Mining is the process of examining a database to look for patterns and correlations, using pattern recognition and statistics.
the process of discovering new information by searching and asking questions of (querying) large databases.
Data mining entails analyzing information for previously undiscovered correlations between two markets. Data mining connections can be made through associations (baseball fans also watch football), sequences (buying wood and then buying paint), forecasting (based on patterns found), and clustering (grouping information in a new way).
Data mining is a set of techniques for analyzing data and extracting hidden information from the data for a variety of purposes.
The process of studying data to search for previously unknown relationships. This knowledge is then applied to achieving specific business goals.
Examining, analyzing, and processing information for specific analysis.
The process of drilling down in the data warehouse to find correlations and trends. This process is used in CRM software applications.
The unguided (or minimally guided) application of a collection of mathematical procedures to a company’s data warehouse in an effort to find “nuggets” in the form of statistical relationships
Using software to discover patterns within a database. It uses approaches such as neural networks, case-based reasoning, and decision-tree algorithms.
The use of sophisticated search engines that use statistical algorithms to discover patterns and correlations in otherwise unrelated data. It's used as a way to find knowledge buried in the vast mountain of information either on the Internet or in a companies own files.
A technique to analyse data in very large databases. Analysis can reveal trends and patterns and can be used to improve vital business processes.
Data mining refers to the actual process of analyzing the data in a data warehouse. The data miner decides what queries are required of the database, and uses a special query language to create the reports.
The process of detecting hidden patterns, trends and associations witin data stored in a large databses like a data warehouse. Typical methods used: neural networks, swarm intelligence, pattern recognition, and statistical analysis.
The primary goal of Data Mining is to extract useful information from databases where that tracking that extracted information was not the original goal of the database. Its practical application is to make better use of existing information in projecting sales and marketing efforts, and can also be very useful when trying to enter new, but related markets.
Extraction of useful information from data sets. Data mining serves to find information that is hidden within the available data.
Data mining uses complex algorithms to search large amounts of data and find patterns, correlation's, and trends in that data. A data-mining application can create a model that can identify buying habits, shopping trends, credit card purchases as well as perform many non-commercial functions.
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.
Searching large volumes of data looking for patterns that accurately predict behavior in customers and prospects.
The process of discovering hidden, previously unknown, and usable information from a large amount of data. This information is represented in a compact form, often referred to as a model.
Application of artificial intelligence to solve marketing problems and aiding forecasting and prediction of marketing data.
Managers or supervisors could use this tool to analyze and explore transactions to find any unusual patterns in the history of a business. http://www.techweb.com/encyclepedia/defineterm?term=data+mining
The process of identifying commercially useful patterns or relationships in databases or other computer repositories through the use of advanced statistical tools.
Data mining is derogatory. It means sorting through a huge volume of data, extracting decision rules that seem to favor one team over another, but without regard to whether or not there is any cause-and-effect relationship. Data mining is the sports-betting equivalent of sitting a huge number of monkeys down at keyboards, and then reporting on the monkeys who happened to type actual words.
using computers to analyze masses of information to discover trends and patterns.
the discovery and modeling of hidden patterns in large amounts of data. Models address why things happened and what is likely to happen next. A user can pose "what-if" questions to a data-mining model that can not be queried directly from the database or warehouse.
Analysis of raw data to find interesting facts about the data and for purposes of knowledge discovery.
Digging through your data for little nuggets. It's a methodical analysis of a pool of your data, so the results are not biased by any preconceived ideas that you may have, such as "my customers really like our product in Pink". The result of this process will hopefully provide you with unknown relationships or patterns in your data.
Analyzing information in a database using tools that look for trends or anomalies without knowledge of the data's meaning. Data mining is crucial in CRM strategies, particularly in e-commerce.
Techniques for finding patterns and trends in large data sets. See also Data Visualization.
Data mining also known as knowledge-discovery in databases (KDD), is the practice of automatically searching large stores of data for patterns which uses computational techniques from statistics and pattern recognition.
Whether formally or informally, every business collects information about its customers. Analyzing this information is a critical part of business intelligence called data mining. The internet and other integrated data systems have increased the opportunities for gathering consumer data. Even small companies can now afford to implement powerful data mining techniques that analyze the attributes and habits of customers. Dissecting and putting massive amounts of consumer information to profitable use is the challenge of data mining. Read more...
The process of finding hidden patterns and relationships in data. For instance, a consumer goods company may track 200 variables about each consumer. There are scores of possible relationships among the 200 variables. Data mining tools will identify the significant relationships.
Data mining is sorting through data to identify patterns and establish relationships. Used as a customer relationship management (CRM) tool, it can take advantage of the huge amount of information gathered by a Web site to look for patterns in user/customer behaviour. The results are used to understand customer behaviour, evaluate the effectiveness of a particular Web site, and help quantify the success of a marketing campaign.
A method of finding data which fits a required pattern, in a large database. The database structure does not have records organized according to that pattern. Used to target customers for highly specific needs or preferences.
Searching through a large volume of statistics to find profitable situations to bet on in the future.
Term encompassing a range of processes used to glean information from databases. Data mining involves statistical and artificial intelligence methods and can reveal information about the typical behavior of groups of people. Data mining is a tool used, for example, by banks, insurance and other companies that collect large amounts of data on their customers.
Related Terms: ETL Analysis of large volumes of relatively simple data to extract important trends and new, higher level information. For example, a data mining program might analyze millions of product orders to determine trends among top-spending customers, such as their likelihood to purchase again, or their likelihood to switch to a different vendor.
A means of extracting previously unknown, actionable information from the growing base of accessible data in data warehouses using sophisticated, automated algorithms to discover hidden patterns, correlations and relationships.
The collection and mathematical analysis of vast amounts of computerized data to discover previously hidden patterns or unknown relationships.
Analyzing information in a database using tools that look for trends or anomalies without knowledge of the data's meaning. Mining a clinical database may produce new insights on outcomes, alternate treatments, or effects of treatment on different races and genders.
Basic process employed to analyze patterns in data and uncover hidden information stored in alphanumeric databases.
The use of automated data analysis techniques to uncover previously undetected relationships among data items.
Transforming raw data into higher-level constructs, such as predictive models, explanatory models, filters or summaries by using algorithms from fields such as artificial intelligence and statistics. Techniques used can range from very simple models, such as arithmetic averages; those of intermediate complexity, for example, linear regression, clustering, decision trees, case-based reasoning and k-nearest neighbor; to very complicated models including neural networks and Bayesian networks.
The extraction of hidden predictive information from large databases.
The process of taking a learning set and applying an algorithm to obtain one or more statistical models. More generally, the semi-automatic extraction of patterns, changes, associations, anomalies, and other statistically significant structures from large data sets. Even more generally, the analysis of data to improve decisions.
Data mining is the process of reviewing and analyzing data for the purpose of identifying common characteristics that may be useful for other purposes.
Searching for patterns in the company database that offer clues to customer needs, preferences, and behaviors.
exploration de données The process of analyzing large volumes of data using pattern recognition or knowledge discovery techniques to identify meaningful trends and relationships represented in data in large databases. Source: BC Government Information Resource Management Glossary
Process of finding patterns and relationships among data. 13.39
A set of techniques for sifting through huge amounts of information held in databases, with the aim of discerning useful trends, facts or associations.
A technique that uncovers new information from existing information by probing mammoth data sets.
A technique of sifting through vast amounts of data to discover trends in customer needs, buying patterns, profitability, and other critical business measurements. Usually requires the construction of a data warehouse.
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. Typical applications include market segmentation, customer profiling, fraud detection, evaluation of retail promotions, and credit risk analysis.
Using specialized software to look for hidden patterns in data - for example, identifying customers with common interests.
Analysis of data in a database using tools which look for trends, or anomalies, without knowledge of the meaning of the data.
Data mining (DM), also called Knowledge-Discovery in Databases (KDD) or Knowledge-Discovery and Data Mining, is the process of automatically searching large volumes of data for patterns using tools such as classification, association rule mining, clustering, etc.. Data mining is a complex topic and has links with multiple core fields such as computer science and adds value to rich seminal computational techniques from statistics, information retrieval, machine learning and pattern recognition.