CambridgeSoft, a leading supplier of desktop and enterprise knowledge management software solutions for life sciences companies, and StatSoft, a leading supplier of desktop and enterprise STATISTICA data analysis and data mining software solutions, announce the execution of a collaboration agreement. The agreement is the start of formal collaborations between the two companies, complementing CambridgeSoft’s Chem & Bio Office suite with integrated graphical and numerical data analysis capabilities of the STATISTICA software.
“It is a natural fit between our organizations,” said Paul Lewicki, CEO of StatSoft, “as we had major joint customers with large, global commitments to both respective product lines, requesting integrations between them.
In September 2008, Rexer Analytics released the results of its Annual Data Mining Software Survey, a survey of the community of data mining professionals across all industries. The survey results report a number of findings to note:
StatSoft (Tulsa, Oklahoma) and REvolution Computing (New Haven, CT) December 8, 2008
As part of its ongoing plans and efforts to enable the use of the R language within STATISTICA, StatSoft has certified REvolution R as compatible with the STATISTICA software platform.
R is a language for statistical computing and graphics. It is open-source software used in practically every graduate-level statistics course. STATISTICA leverages R to assist customers who need access to the existing vast selection of specialized statistical procedures written in R. REvolution Computing offers open-source products and solutions for analytics including REvolution R, which is an optimized, validated, and supported distribution of R.
StatSoft's article covering data mining applications for process improvement in discrete, batch and continuous manufacturing is cover story in December 2008 issue of Quality Digest.
Dr Thomas Hill, Robert Eames and Sachin Lahoti discuss the differences between traditional statistical analysis and data mining methods. The article stresses some advantages of data mining when applied to manufacturing applications.