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DATA MINING CLUSTERING FOR CREDIT CARD CORP
Table of Contents
Introduction
Data Mining Concepts
The Data Mining Process
Data Preparation
Data Clustering
Applications of Clustering in Text Mining
Text Feature Extraction
Data Preparation for k-Means Algorithm
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.
Data Preparation
Data preparation is something that for data for mining that must exist within the table of view.
The more information can be stored to the separate rows.
A unique capability that can be used to the data mining and its supports for the dimensioned data though table transformations.
Additionally, the data mining that can be used to mine the unstructured data.
The preparation for that data must be a key factor for the data mining projects.
The data should be used to clean the eliminating the inconsistencies and support to the data mining applications.
Data Clustering
When you use the clustering the data then the numbers indicate how often each document can contain each word.
You can check the table to that documents that are probably about one of three main themes: Astronomy and animals.
However, the last two documents are a bit strange.
Documents M contains words with the astronomy and movie stars and Document N contains to the many words having the both astronomy and animals.
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.
Text Feature Extraction
Feature extraction is used for text transportation at two different stages using the text mining process:
A feature extraction process must be performed on the text document. If you can mine the data that should be documented to the feature extraction. This is basically a pre-processing step transforms to the text documents. And that text documents will be converted into the small units of text called features or terms for mining the data.
Basically the transformation process generates the large data numbers of text features from the text documents.
Data Preparation for k-Means Algorithm
k-Means Algorithm: The k-means algorithm basically focus on the distance based clustering algorithm that is basically used to partitions onto the data to predetermine the number of clusters provided to their distinct cases.
Data Preparation for k-Means: The automatic data preparation can be used to performing onto the k-means normalizations. The missing values can be used for the columns with the simple data types that can be used for the interpreted randomly.
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.
The clustering process and the test data referred to the dissertation report and that are mostly generic.
75653/Report.docx
DATA MINING CLUSTERING FOR CREDIT CARD CORP
10
Executive Summary
The demand for the credit cards is growing over the time. It will give the number of credit cards in the system. The distribution has been increased for the local banks that can give the aggressive for the debit cards. Using the data mining techniques, it will give the cover-up for the duplication of users and it will concern the industry, which can give the number of active users. The research has been carried out to the card industry so that can include the types of providers and feature of that cards will give the major acceptability for the cards among to the consumers. It also involves the finding consumer’s perception towards the different providers. You can use the data mining techniques that can find the consumers perception towards the different age group and occupation and it will help to get the credit card corporation technique.
Table of Contents
Executive Summary    2
Introduction    4
Data Mining Concepts    5
Automatic Discovery    5
Prediction    5
Grouping    5
Actionable Information    5
The Data Mining Process    6
Problem Definition    6
Data Gathering and Preparation    6
Model Building and Evaluation    6
Data Preparation    6
Clustering Data    7
Applications of Clustering in Text Mining    8
Simple clustering    8
Taxonomy generation    8
Topic extraction    8
Text Feature Extraction    9
Algorithms    10
k-Means    10
Data Preparation for k-Means    10
Features of a DME Connection    10
Execute Mining Tasks    10
Retrieve DME Capabilities and Metadata    11
Retrieve Version Information    12
Sample Data    12
Result    13
Conclusion    14
Reference    15
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. A software application that can cluster the call notes periodically and show the report with cluster statistics is being developed. In this dissertation, we talk about an investigation into the proof of concept development of text clustering infrastructure using JDM and ODM.
Data Mining Concepts
Automatic Discovery
The building models of data science techniques can accomplish the data mining concepts. The model can be uses a different types of algorithms to act on a set of data. This process can be applying to the model to new data is called scoring in a data mining.
Prediction
In data mining, the many forms are predictive. The prediction statement can be given as the associated probability. The prediction is also called as the confidence to the model. The predictive data mining can be generated to the rules, which are implying to given the outcome.
Grouping
In data mining to identify the other forms, we focus on identifying on the natural groupings into the data.
Actionable Information
In the data mining process, the actionable information gives the more volumes of data. For example, a town planner may be used to the model can predicts the...