Zheng Ying. (2009). Analysis of credit card data based on data mining technique. Mémoire de maîtrise, Université du Québec à Chicoutimi.
In recent years, large amounts of data have accumulated with the application of database systems. Meanwhile, the requirements of applications have not been confined in the simple operations, such as search and retrieval, because these operations were not helpful in finding the valuable information from the databases. The hidden knowledge is hard to be handled by the present database techniques, so a great wealth of knowledge concealed in the databases is not developed and utilized mostly.
Data mining aimed at finding the essential significant knowledge by automatic process of database. DM technique was one of the most challenging studies in database and decision-making fields. The data range processed was considerably vast from natural science, social science, business information to the data produced from scientific process and satellite observation. The present focuses of DM were changed from theories to practical application. Where the database existed, there were many projects about DM to be studied on.
The paper concentrated on the research about data information in credit card by DM theories, techniques and methods to mine the valuable knowledge from the card. Firstly, the basic theories, key algorithms of DM techniques were introduced. The emphases were focused on the decision tree algorithms, neural networks, X-means algorithm in cluster and Apriori algorithm in association rule by understanding the background of bank and analyzing the knowledge available in the credit card. A preliminary analysis of credit card information, Industry and Business Bank at Tianjin Department, was performed based on the conversion and integration of data warehouse. The combined databases including information of customers and consumptive properties were established in accordance with the idea of data-warehouse. The data were clustered by iT-means algorithm to find valuable knowledge and frequent intervals of transaction in credit card. Back propagation neural networks were designed to classify the information of credit card, which played an important role in evaluation and prediction of customers. In addition, the Apriori algorithm was achieved to process the abovementioned data, which could establish the relations between credit information of customers and consumption properties, and to find the association rule among credit items themselves, providing a solid foundation for further revision of information evaluation.
Our work showed that DM technique made great significance in analyzing the information of credit card, and laid down a firm foundation for further research in the retrieval information from the credit card.
|Type de document:||Thèse ou mémoire de l'UQAC (Mémoire de maîtrise)|
|Lieu de publication:||Chicoutimi|
|Programme d'étude:||Maîtrise en informatique|
|Nombre de pages:||83|
|Sujets:||Sciences naturelles et génie > Sciences mathématiques > Informatique|
|Département, module, service et unité de recherche:||Départements et modules > Département d'informatique et de mathématique > Programmes d'études de cycles supérieurs en informatique|
|Directeur(s), Co-directeur(s) et responsable(s):||Ming, Cheng|
|Mots-clés:||Cartes de crédit, Credit cards, Bases de données, Databases, Exploration de données (Informatique), Data mining|
|Déposé le:||01 janv. 2009 12:34|
|Dernière modification:||20 sept. 2011 15:38|
Éditer le document (administrateurs uniquement)