Visual and analytical mining Visual and analytical mining of transactions data of transactions data

for production planning for production planning and marketingand marketing

Gurdal Ertek, Can Kuruca, Cenk Aydin,Gurdal Ertek, Can Kuruca, Cenk Aydin, Besim Ferit Erel, Harun Dogan, Mustafa Duman, Besim Ferit Erel, Harun Dogan, Mustafa Duman,

Mete Ocal, Zeynep Damla OkMete Ocal, Zeynep Damla Ok

SABANCI UNIVERSITYSABANCI UNIVERSITY

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 --- k-means algorithm– Comparison --- Parallel coordinate plot

Motivation: Explosion of Data

• Data from marketing– Barcode systems, accounting software, ERP

software, e-commerce data (B2B and B2C)

• Data from manufacturing– CIM systems, barcode, radio frequency

technologies

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

Sales Transaction Data

• Collected and archieved in almost every firm

• Essential input for both marketing and production planning

• Framework and prototype implementation CuReMa

Literature Review

• …-1990’s: Scatterplot, boxplot, …

• 1990-2000’s: Information visualization

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Proposed Framework

• Filtering

• Clustering

• Comparison

Implementation of the Proposed Framework:

CuReMa

Filtering: Interactive Visual QueryingFiltering: Interactive Visual Querying

Clustering: Analytical Data MiningClustering: Analytical Data Mining

Comparison: Visual Data MiningComparison: Visual Data Mining

Comparison: Visual Data MiningComparison: Visual Data Mining

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: Self-organizing maps

• Other criteria for clustering can be implemented– Ex: Recency and frequency of purchases

• Localization issues– Ex: Inflation and local holidays