Clementine is widely regarded as the leading enterprise data mining workbench because it quickly delivers a measurable ROI. Clementine�s open architecture enables rapid modeling utilizing existing IT investments. In Clementine 9.0, this dedication to maximizing data mining returns within large commercial and public sector organizations continues. Driven by extensive customer feedback, the latest version of Clementine further enhances enterprise support and modeling productivity.
Clementine 9.0 increases support for data mining within enterprise operations by tightening integration with both leading database vendors and other SPSS offerings. Improved in-database mining integrations support modeling with Oracle Data Mining and IBM Intelligent Miner�enabling you to build and score models directly within these popular databases. Other data mining workbenches force you to move data into their proprietary formats�adding unnecessary steps that significantly delay modeling progress.
Integration improvements with other SPSS offerings make Clementine the most comprehensive data mining solution available. Other data mining workbenches the lack key functionality required to execute data mining as an enterprise business process. Clementine provides integrated data mining capabilities for the entire data mining process�from text and Web data preparation to deployment options for nearly every operating environment.
Clementine�s visual workflow interface enables rapid predictive modeling to help you deliver value to your organization as quickly as possible. Data preparation, visualization, and decision tree modeling improvements further accelerate data mining productivity.
Up to 90 percent of data mining project time is spent on data preparation. The new one-step partition node helps you prepare data much more quickly�splitting data into separate samples for the training, testing, and validation stages of model building. This feature dramatically decreases data mining �time-to-value�.
Decrease data preparation time by automatically comparing training and testing data sets. Click image to enlarge.
New visualization techniques help advance the data mining discovery process�guiding the way to the best results. Clementine�s advanced visualization tool set includes bar charts, pie charts, boxplots, scatterplot matrices (SPLOM), parallel coordinate maps, heat maps, and other types of maps, as well as panel plots and linkage analysis plots.
Visualization improvements help you discover the insights needed to produce better models. Click image to enlarge.
The ability to use Clementine�s visual workflow interface to build and test numerous models quickly helps you find the best model in less time. Clementine 9.0 expands this functionality to decision tree modeling with three new algorithms and improved interactivity. Three new algorithms�CHAID (CHi-squared Automatic Interaction Detection), Exhaustive CHAID, and QUEST (Quick, Unbiased, Efficient Statistical Tree)�provide the widest choice of decision trees available.
Decision tree modeling enhancements also include support for interactive building and user-defined splitting of decision tree models. Build decision trees interactively, allowing C&RT, CHAID, and QUEST to choose the best split at each level, or customize splits by applying your business knowledge to refine the tree.
White Paper: Clementine Data
Mining Workbench
Bloor Research
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leadership in data
mining market.
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