Clementine is a data mining application that allows users to import, manipulate, model and visualize data through a node-based visual interface. It offers a variety of data mining techniques and pre-built solutions. The user interface consists of a stream canvas to build data flows, toolbars, palettes of nodes for different data operations, and areas to manage streams, outputs and models. Clementine templates provide pre-built workflows for common data mining tasks like web mining and customer analytics. An example demonstrates importing data, performing field operations, generating models and exporting results.

1. Shri S’ad Vidya Mandal Institute Of Technology
NAME ENROLLMENT NO.
Raj Bhavsar 150450116009
Jainam Kapadiya 150450116015
Topic:- Clementine Tool
Subject:- Data Mining and Business Intelligence(2170715)
Presented by:-

2. Clementine
As a data mining application, Clementine offers a strategic approach to finding useful
relationships in large datasets.
Clementine provides wide range of data mining techniques, along with pre-built vertical
solutions, in an integrated and comprehensive manner, with a special focus on visualization
and case-of-use.
 Working with Clementine is a three-step process of working with data.
• First, you read data into Clementine,
• Then, run the data through a series of manipulations,
• And finally, send the data to a destination.

3. Menu Bar
Tool Bar
Stream Canvas
Palettes
Nodes
Clementine User Interface

4.  The stream canvas is the largest area of the Clementine window and is where you will
build and manipulate data streams.
 Streams are created by drawing diagrams of data operations relevant to your business
on the main canvas in the interface. Each operation is represented by an icon or node,
and the nodes are linked together in a stream representing the flow of data through
each operation.
 We can work with multiple streams at one time in Clementine, either in the same
stream canvas or by opening a new stream canvas. During a session, streams are
stored in the Streams manager, at the upper right of the Clementine window.
Stream Canvas

5. Nodes Palettes
Most of the data and modeling tools in Clementine reside in the Nodes Palette,
across the bottom of the window below the stream canvas.
For example,

6.  Palette tab contains:-
• Sources:- Nodes bring data into Clementine.
• Record Ops. Nodes perform operations on data records, such as selecting, merging, and
appending.

7. • Field Ops. Nodes perform operations on data fields, such as filtering, deriving new
fields, and determining the data type for given fields.
• Graphs. Nodes graphically display data before and after modeling. Graphs include plots,
histograms, web nodes, and evaluation charts.

8. • Modeling. Nodes use the modeling algorithms available in Clementine, such as neural nets,
decision trees, clustering algorithms, and data sequencing.
• Output. Nodes produce a variety of output for data, charts, and model results, which can be
viewed in Clementine or sent directly to another application, such as SPSS
or Excel.

9. Streams , Outputs and Model Manager
Stream Tab:- This tab is used to open, rename, save, and delete the streams created in
a session as Shown in Fig (a).
Outputs tab:- This tab contains a variety of files, such as graphs and tables, produced
by stream operations in Clementine. You can display, save, rename, and close the
tables, graphs, and reports listed on this tab as shown in fig(b).
Models tab:- This tab contains all model nuggets, which are models generated in
Clementine, for the current session. These models can be browsed directly from the
Models tab or added to the stream in the canvas as shown in figure (c).
Fig (a) Fig (b) Fig (c)

10. Project Tools
 Projects tool are used to create and manage data mining projects.
 CRISP-DM tab:- This tab provides a way to organize projects according to the
Cross-Industry Standard Process for Data Mining, an industry-proven,
nonproprietary methodology as shown in fig (d).
 Classes tab:- This tab provides a way to organize your work in Clementine
categorically—by the types of objects you create. This view is useful when taking
inventory of data, streams, and models as shown in fig (e).
Fig (d) Fig (e)

11.  Clementine Application Templates, also known as CATs, are available for the
following types of activities:
• Web-mining
• Fraud detection
• Analytical CRM
• Telecommunications analytical CRM
• Microarray analysis
Clementine Application Templates (CATs)

12. Example
1. Start Clementine Tool.
2. Now taking Dataset of Employ Data as Excel node from sources tab.

13. 3. Taking Type node from Field Ops tab.
4. Connecting it with the dataset.

14. 5. Now connecting Type node with the table node as an Output.
Output

15. 6. Now taking a graph of Web.
7. Connecting it with Type node.

16. 8. Taking Apriori Algorithm and connecting it with Type node.

17. 9. Executing Apriori algorithm it will give output in models tab follows:-
10. Dragging Output from models tab to the stream canvas.
Output

18. 11. Now taking Excel file for exporting the employ data file into another file.
Output