The document proposes a framework that combines visual and analytical mining of transaction data for production planning and marketing. The framework uses interactive pie charts for filtering, k-means clustering for analysis, and parallel coordinate plots for comparison. It was implemented in a prototype called CuReMa. Future work could incorporate additional visualizations, data fields, clustering algorithms, and criteria to further analyze enterprise transaction data.

1. Visual and analytical mining Visual and analytical mining
of transactions data of transactions data
for production planning for production planning
and marketing and marketing
Gurdal Gurdal Ertek Ertek, Can , Can Kuruca Kuruca, , Cenk Cenk Aydin Aydin, ,
Besim Besim Ferit Ferit Erel Erel, , Harun Harun Dogan Dogan, Mustafa , Mustafa Duman Duman, ,
Mete Mete Ocal Ocal, , Zeynep Zeynep Damla Damla Ok Ok
SABANCI UNIVERSITY SABANCI UNIVERSITY

2. Introduction
• Motivation
– Large amounts of enterprise data available
• Data mining
– Deriving necessary and meaningful information out of
data
• Framework combining visual and analytical data
mining
– Filtering ­­­ Interactive pie charts
– Clustering ­­­ k­means algorithm
– Comparison ­­­ Parallel coordinate plot

3. Motivation: Explosion of Data
• Data from marketing
– Barcode systems, accounting software, ERP
software, e­commerce data (B2B and B2C)
• Data from manufacturing
– CIM systems, barcode, radio frequency
technologies

4. Data Mining
• Effective collection, management, reporting,
interpretive analysis and mining of enterprise data:
– Establishing effective control of manufacturing
activities
– Achieving effective production planning and increased
sales, and consequently increasing the firm’s
profitability
– Increasing customer satisfaction by offering and timely
delivering them products that they are willing to
purchase.
• CRM: Customer Relationship Management

5. Sales Transaction Data
• Collected and archieved in almost every
firm
• Essential input for both marketing and
production planning
• Framework and prototype implementation
CuReMa

6. Literature Review
• …­1990’s: Scatterplot, boxplot, …
• 1990­2000’s: Information visualization
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7. Proposed Framework
• Filtering
• Clustering
• Comparison

8. Implementation of the
Proposed Framework:
CuReMa

9. Filtering: Interactive Visual Querying Filtering: Interactive Visual Querying

10. Clustering: Analytical Data Mining Clustering: Analytical Data Mining

11. Comparison: Visual Data Mining Comparison: Visual Data Mining

12. Comparison: Visual Data Mining Comparison: Visual Data Mining

13. Future Work
• Other visual metaphors and analytical approaches
can be used to extend the framework
– Ex: Drawing association rules
• Other data fields can be incorporated
– Ex: Ages and income levels of customers
• Other clustering algorithms can be used
– Ex: Self­organizing maps
• Other criteria for clustering can be implemented
– Ex: Recency and frequency of purchases
• Localization issues
– Ex: Inflation and local holidays