Data Mining

Data Mining, also called KnowledgeDiscovery, is a general term for a variety of interlocking technologies that, used together, find, isolate, and quantify patterns hidden in large and often disparate collections of data. As a general knowledge extraction process, its primary goal is the discovery of nontrivial and potentially valuable hidden in local files, databases, and in repositories scattered across distributed networks.

Employing a wide spectrum of statistical analysis, machine learning, graph theory, and advanced computer science techniques data mining uncovers often subtle patterns and time varying relationships buried deep in the morass of data. From these relationships and shifting patterns it evolves a set of rules that predict and classify future behaviors.

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.

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While earlier generations of data mining approaches generated static reports and statistical descriptions, the newest breed of data mining systems (such as the mechanisms in Scianta Intelligence’s Adaptive Intelligence Platform) generate powerful models that often tightly integrated with client applications. These models can take on a wide variety of forms: if-then rule based knowledge systems, adaptive feed-back models that incorporate machine learning, linkage and affinity modes that discover shared connections and relationships, statistical models, regression models for time-series prediction, and classification model.

Typical uses of data mining include supply chain management, customer relationship analysis and profiling, fraud and anomalous behavior discovery, risk assessment, inventory optimization, and customer cross-selling and profitability.