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Clementine Server A data mining software for business solution

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Clementine Server A data mining software for business solution. What is Clementine Server. Clementine Server is a large scale, distributed Data mining software package with  fast and scalable performance,  user-friendly visual workflow interface,  powerful analytical techniques. .

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Clementine Server A data mining software for business solution
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  1. Clementine ServerA data mining software for business solution

  2. What is Clementine Server Clementine Server is a large scale, distributed Data mining software package with fast and scalable performance, user-friendly visual workflow interface, powerful analytical techniques. .

  3. Brief Introduction to Clementine History of Clementine Series: • Clementine Series was initiated by Integral Solutions Ltd in 1994. • SPSS took over ISL in 1998. • SPSS introduced the latest version - Clementine Server 5.1 with distributed architecture in earlier 1999. Old version vs New version: Stand-alone application architecture vs Distributed architecture

  4. Architecture Stand-alone application architecture

  5. Architecture Distributed Architecture

  6. Capabilities Fast and scalable Performance with Distributed Architecture

  7. Capabilities Interactive and efficient mining process with Visual Workflow Interface  Visual Programming • Enables non-technical users to solve business problems.  Easy & Intuitive desktop • Allows rapid experimentation, and creative development of models.

  8. Palette forGeneratedModels Desktop ObjectPalette

  9. Clementine will report the predicted accuracy of the two used algorithms: neural network and rule induction.

  10. The target data will run through the model to produce knowledge Data Source Useful Knowledge

  11. Capabilities Multiple model building techniques: - Rule Induction - Graph - Clustering - Association Rules - Linear Regression - Neural Networks

  12. Functionalities  Classification Rule Induction, neural Networks  Association Rule Induction, Apriori  Clustering Kohonen Networks, Rule Induction  Sequence Rule Induction, Neural Networks, LinearRegression  Prediction Rule Induction, Neural Networks

  13. Applications  Predict market share  Detect possible fraud  Locate new retail sites  Assess financial risk  Analyze demographic trends and patterns

  14. Limitation  Clementine does not have effective incremental methods to update mining results.  Clementine does not support certain data sources i.e. Dbase, Foxpro.

  15. Summary • Clementine Server Fast and scalable performance on large datasets - unique distributed architecture Rapid and creative development of models - user-friendly visual workflow interface Multiple applications - various modeling techniques

  16.  Peng Wang  Chong Zhang  Hang Cui Clementine Study Group