Data mining and predictive modeling software and consulting services provider Salford Systems unveiled its new Salford Predictive Modeling (SPM) software suite at the ongoing NCDM 2010, the conference for engaging customers using data and technology. SPM provides businesses, institutions and government agencies with an accurate and ultra-fast platform for developing predictive, descriptive and analytical models from large and complex databases.
SPM technology accelerates accurate, robust model generation by automatically sifting through such databases to isolate significant patterns and relationships. The programme claims to be easy to use for both technical and non-technical users. In addition to powerful new proprietary automation and modeling capabilities, the Salford Predictive Modeling Suite includes the company's four data mining products - CART (Classification and Regression Trees), TreeNet, MARS (Multivariate Adaptive Regression Splines) and RandomForests.
CART analyses large and complex databases to generate classification and regression trees that quickly reveal important data patterns and relationships that could remain hidden using other analytical tools.
TreeNet is the proprietary technology underlying major recent advances in fraud detection, targeted marketing, and risk modeling. Its flexible, easy to use and learn technology enables users to create super-accurate, targeted marketing models and identify ultra-high lift segments with little analyst supervision.
MARS is ideal for users who prefer results in a form similar to traditional regression while capturing essential nonlinearities and interactions. Its flexibility permits MARS to trace out non-linear patterns detected in the data. It can predict continuous numeric outcomes as well as probability models for yes/no outcomes.
RandomForests is best suited for analysing complex data structures embedded in small-to-moderate data sets for deep understanding. It uses the power of multiple alternative analyses, randomisation strategies and ensemble learning to produce accurate models, insightful variable importance rankings and accurate reporting.
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