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75309/Presentation.pptxDATA MINING CLUSTERING FOR CREDIT CARD CORP
Table of Contents
Introduction
Data Mining Concepts
The Data Mining Process
Applications of Clustering in Text Mining
Results
Conclusion
Introduction
The Credit Card Corp uses SAS enterprise miner, a commercial data mining tool in their credit card business applications, a part of CRMD, for tasks like fraud detection, risk minimization, anticipation of resource demands, seeking increase response rates for marketing campaigns and curbing customer attrition etc.
Aware of growing industry and academia support for JDM and ODM, The Credit Card Corp wants us to build an in-house data mining capability to perform tasks mentioned above, for them, using JDM and/or ODM.
The Credit Card Corp offers an online call center application to service millions of their credit card calls. The call center representatives enter call notes and save them in the database.
The Credit Card Corp management team is interested in knowing the top reasons for calls in real time, especially whether there are new issues that generate a large call volume.
Data Mining Concepts
Automatic Discovery: Data mining is accomplished by building models. A model uses an algorithm to act on a set of data.
Prediction: Many forms of data mining are predictive. For example, a model might predict income based on education and other demographic factors.
Grouping: Other forms of data mining identify natural groupings in the data.
Actionable Information: Data mining can derive actionable information from large volumes of data. For example, a town planner might use a model that predicts income based on demographics to develop a plan for low-income housing.
A car leasing agency might a use model that identifies customer segments to design a promotion targeting high-value customers.
The Data Mining Process
This initial phase of a data mining project focuses on understanding the project objectives and requirements. Once you have specified the project from a business perspective, you can formulate it as a data mining problem and develop a preliminary implementation plan.
The data understanding phase involves data collection and exploration. As you take a closer look at the data, you can determine how well it addresses the business problem. You might decide to remove some of the data or add additional data.
In this phase, you select and apply various modelling techniques and calibrate the parameters to optimal values. If the algorithm requires data transformations, you will need to step back to the previous phase to implement them.
Applications of Clustering in Text Mining
Simple clustering: This refers to the creation of clusters of text features. For example: grouping the hits returned by a search engine.
Taxonomy generation: This refers to the generation of hierarchical groupings. For example: a cluster that includes text about car manufacturers is the parent of child clusters that include text about car models.
Topic extraction: This refers to the extraction of the most typical features of a group. For example: the most typical characteristics of documents in each document topic.
Results
This chapter mentions the intermediary results and final output during the data mining process carried out in the project work.
The user logs in on the admin console and chooses the application, chooses the algorithm and clicks on Demo Text Clusters.
Based on nature of comment, the prediction on the customer service quality and product acceptability by customers for usage of the statistics and histogram of comments in Oracle Data Miner.
Conclusion
An effort has been made to investigate the proof-of-concept work for building text clustering infrastructure in a call center application using data mining concepts.
The efforts involved include learning data mining concepts. Since this work is being done for our client, The Credit Card Corp whose policy mentions about ensuring the confidentiality of their data, the actual text clustering process and real-time data used are masked.
And, the clustering process and the test data referred to, in this dissertation report are...