|Papers Topic List|
|January 2013||Big Data Analytics: Benchmarking SAS, R, and Mahout (PDF)
This paper benchmarks SAS and open-source products to analyze big data by modeling four classification problems from real customers. The products that were benchmarked are SAS Rapid Predictive Modeler (a component of SAS Enterprise Miner), SAS High-Performance Analytics Server (using Hadoop), R and Apache Mahout. Results were compared in terms of model quality, modeler effort, scalability and completeness.
|April 2009||The Next Generation: SAS Enterprise Miner 6.1 (PDF)
SAS Enterprise Miner 6.1 includes a vast range of new features including integration with the SAS 9.2 System, extended data preparation, enhanced modeling capabilities, improved reporting, and new scoring alternatives. This paper provides an overview of the SAS Enterprise Miner 6.1new features and uses an example of mining data about charitable donations to illustrate some of these features.
|October 2008||Tune into the Voice of Your Customer with Voice Mining (PDF)
Analyzing audio data can help you identify the reasons for call spikes, the effectiveness of marketing campaigns, the competitors most mentioned by your clients, why certain products sell more than others, and predict the customer satisfaction level of every interaction.
|March 2008||Two-Stage Variable Clustering for Large Data Sets (PDF)
In data mining, principal component analysis is a popular dimension reduction technique. It also provides a good remedy for the multicollinearity problem, but its interpretation of input space is not as good. To overcome the interpretation problem, principal components (cluster components) are obtained through variable clustering, which was implemented with PROC VARCLUS.