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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 a ...
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 ...
SEMMA mainly focuses on the modeling tasks of data mining projects, leaving the business aspects out (unlike, e.g., CRISP-DM and its Business Understanding phase). Additionally, SEMMA is designed to help the users of the SAS Enterprise Miner software.
Data mining is a particular data analysis technique that focuses on statistical modeling and knowledge discovery for predictive rather than purely descriptive purposes, while business intelligence covers data analysis that relies heavily on aggregation, focusing mainly on business information. [4]
Data management comprises all disciplines related to handling data as a valuable resource, it is the practice of managing an organization's data so it can be analyzed for decision making. [ 1 ] Concept
OLAP is part of the broader category of business intelligence, which also encompasses relational databases, report writing and data mining. [3] Typical applications of OLAP include business reporting for sales, marketing , management reporting, business process management (BPM), [ 4 ] budgeting and forecasting , financial reporting and similar ...
Knowledge discovery from existing software systems, also known as software mining is closely related to data mining, since existing software artifacts contain enormous value for risk management and business value, key for the evaluation and evolution of software systems. Instead of mining individual data sets, software mining focuses on ...
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.