Knowledge Discovery in Plastic Cards Transactions by Using Data Mining

2012 ◽  
Vol 488-489 ◽  
pp. 1466-1472
Author(s):  
Ehsan Saghehei ◽  
Farshad Farahani Deljoo ◽  
Mehrdad Hamidi Hedayat ◽  
Yazdan Khoshjahan
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Value Creation ◽  
Real Life ◽  
Chi Square ◽  
Clustering Techniques ◽  
Interaction Detection ◽  
Customer Profiling ◽  
Using Data ◽  
Original Approach

Today with swift growing of plastic cards industry in the world, variety and volume of data stored in the database is growing strongly, this issue reminds the growing need of banks and financial institutions in applying knowledge discovery processes on value creation services. The original approach of this paper, is step by step implementing process of data mining in real-life transaction of debit cards, with the aim of customer profiling. In this study profiling is applied with two approaches of explorative and predictive analysis. In explorative model SOM and TwoStep clustering techniques are used. Also in predictive model four decision tree techniques are applied, the C5.0, Chi-square Automatics Interaction Detection (CHAID), Quest, classification and regression. Finally, the optimal models details are more analyzed to discover the knowledge in transactions done.

Suspicious Behavior Detection in Debit Card Transactions using Data Mining

2015 ◽  
Vol 28 (3) ◽  
pp. 1-14 ◽  
Author(s):  
Ehsan Saghehei ◽  
Azizollah Memariani
Keyword(s):  
Data Mining ◽  
Hybrid Model ◽  
Real Life ◽  
Final Analysis ◽  
Self Organizing Map ◽  
High Ability ◽  
Detection Problem ◽  
Behavior Detection ◽  
Using Data ◽  
Debit Cards

The approach used in this paper is an implementation of a data mining process against real-life transactions of debit cards with the aim of detecting suspicious behavior. The framework designed for this purpose has been obtained through merging supervised and unsupervised models. First, due to unlabeled data, Twostep and Self-Organizing Map algorithms have been used in clustering the transactions. A C5.0 classification algorithm has been applied to evaluate supervised models and also to detect suspicious behaviors. An innovative plan has been designed to evaluate hybrid models and select the most appropriate model for the solution of the fraud detection problem. The evaluation of the models and the final analysis of the data took place in four stages. The appropriate hybrid model was selected from among 16 models. The results show a high ability of selected model in detecting suspicious behavior in transactions involving debit cards.

scholarly journals Aplikasi Data Mining Dengan Metode CHAID Dalam Menentukan Status Kredit

2020 ◽  
Vol 18 (1) ◽  
pp. 29
Author(s):  
Muhammad Rizki ◽  
Muhammad Isnaini Hadiyul Umam ◽  
Muhammad Luthfi Hamzah
Keyword(s):  
Data Mining ◽  
Chi Square ◽  
Interaction Detection ◽  
Detection Analysis

Seiring dengan digalakkannya Industrial 4.0, data mining menjadi topik yang hangat untuk bahas dikalangan peneliti. Perkembangan teknologi yang begitu cepat memaksa kita untuk dapat mengambil keputusan dengan cepat pula. Kredit macet menjadi salah satu resiko terbesar lembaga keuangan. Resiko kredit macet ini wajib diminimalisir dengan menganalisa faktor status nasabah berdasarkan data personalnya, sehingga dapat dilakukan klasifikasi berdasarkan  hubungan antar faktor tersebut. Salah satu kunci utama memenangkan persaingan pasar yaitu dengan menentukan target pasar. Data mining menyediakan banyak alat bantu untuk klasifikasi, salah satunya dengan menggunakan metode analisis CHAID (Chi-square Automatic Interaction Detection Analysis). Diagram pohon keputusan yang dihasilan dari Analisis CHAID dapat memberikan informasi tentang derajat hubungan antara variable independent dan dependent, serta informasi tentang karakteristik masing-masing kategori. Dalam hal ini, analisis CHAID digunakan untuk menentukan klasifikasi nasabah berdasarkan status kredit nasabah sebagai variable terikat dan data pribadi nasabah sebagai variable bebas. Dengan menggunakan uji Chi-square, dari total 7 variables independent, hanya 5 variable yang signifikan dengan variable dependent. Variable-variable tersebut adalah variable independent usia, pekerjaan, pendidikan, jangka waktu dan jumlah pinjaman. Berdasarkan hasil analisis CHAID didapatkan empat kelas. Kelas nasabah dengan pekerjaan sebagai (Aparatur Sipil Negara) ASN merupakan kelas yang memiliki resiko kredit macet yang paling minimal.

Data Mining and Knowledge Discovery

2011 ◽  
pp. 35-47 ◽  
Author(s):  
Andi Baritchi
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Business Processes ◽  
Knowledge Discovery In Databases ◽  
Business World ◽  
Useful Knowledge ◽  
Customer Support ◽  
Customer Base ◽  
Sales And Marketing ◽  
Customer Profiling

In today’s business world, the use of computers for everyday business processes and data recording has become virtually ubiquitous. With the advent of this electronic age comes one priceless by-product — data. As more and more executives are discovering each day, companies can harness data to gain valuable insights into their customer base. Data mining is the process used to take these immense streams of data and reduce them to useful knowledge. Data mining has limitless applications, including sales and marketing, customer support, knowledge-base development, not to mention fraud detection for virtually any field, etc. “Data mining,” a bit of a misnomer, refers to mining the data to find the gems hidden inside the data, and as such it is the most often-used reference to this process. It is important to note, however, that data mining is only one part of the Knowledge Discovery in Databases process, albeit it is the workhorse. In this chapter, we provide a concise description of the Knowledge Discovery process, from domain analysis and data selection, to data preprocessing and transformation, to the data mining itself, and finally the interpretation and evaluation of the results as applied to the domain. We describe the different flavors of data mining, including association rules, classification and prediction, clustering and outlier analysis, customer profiling, and how each of these can be used in practice to improve a business’ understanding of its customers. We introduce the reader to some of today’s hot data mining resources, and then for those that are interested, at the end of the chapter we provide a concise technical overview of how each data-mining technology works.

Data-Mining Techniques for an Analysis Of Non-Conventional Methodologies

2010 ◽  
pp. 82-91
Author(s):  
William Claster ◽  
Nader Ghotbi ◽  
Subana Shanmuganathan
Keyword(s):  
Data Mining ◽  
Alternative Medicine ◽  
Knowledge Discovery ◽  
Western Medicine ◽  
Musculoskeletal Complaints ◽  
Scientific Methods ◽  
Using Data ◽  
Data Elements ◽  
Discovery Algorithms

Some common methodologies in our everyday life are not based on modern scientific knowledge but rather a set of experiences that have established themselves through years of practice. As a good example, there are many forms of alternative medicine, quite popular, however difficult to comprehend by conventional western medicine. The diagnostic and therapeutic methodologies are very different and sometimes unique, compared to that of western medicine. How can we verify and analyze such methodologies through modern scientific methods? We present a case study where data-mining was able to fill this gap and provide us with many tools for investigation. Osteopathy is a popular alternative medicine methodology to treat musculoskeletal complaints in Japan. Using data-mining methodologies, we could overcome some of the analytical problems in an investigation. We studied diagnostic records from a very popular osteopathy clinic in Osaka, Japan that included over 30,000 patient visits over 6 years of practice. The data consists of some careful measurements of tissue electro-conductivity differences at 5 anatomical positions. Data mining and knowledge discovery algorithms were applied to search for meaningful associations within the patient data elements recorded. This study helped us scientifically investigate the diagnostic methodology adopted by the osteopath.

scholarly journals Data mining and mall users profile

2012 ◽  
Vol 12 (1) ◽  
pp. 195-207 ◽  
Author(s):  
Marina Romeo Delgado ◽  
Núria Codina Mata ◽  
Montserrat Yepes Baldó ◽  
José Vicente Pestana Montesinos ◽  
Joan Guardia Olmos
Keyword(s):  
Data Mining ◽  
Empirical Research ◽  
Consumer Response ◽  
Demographic Variables ◽  
Online Questionnaire ◽  
Chi Square ◽  
Effective Strategies ◽  
Interaction Detection ◽  
Chaid Analysis ◽  
Behavioral Variables

Marketing scholars have suggested a need for more empirical research on consumer response to malls, in order to have a better understanding of the variables that explain the behavior of the consumers. The segmentation methodology CHAID (Chi-square automatic interaction detection) was used in order to identify the profiles of consumers with regard to their activities at malls, on the basis of socio-demographic variables and behavioral variables (how and with whom they go to the malls). A sample of 790 subjects answered an online questionnaire. The CHAID analysis of the results was used to identify the profiles of consumers with regard to their activities at malls. In the set of variables analyzed the transport used in order to go shopping and the frequency of visits to centers are the main predictors of behavior in malls. The results provide guidelines for the development of effective strategies to attract consumers to malls and retain them there.

A Query Language for Mobility Data Mining

2011 ◽  
Vol 7 (1) ◽  
pp. 24-45 ◽  
Author(s):  
Roberto Trasarti ◽  
Fosca Giannotti ◽  
Mirco Nanni ◽  
Dino Pedreschi ◽  
Chiara Renso
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Mobile Communications ◽  
Query Language ◽  
Real Life ◽  
Transportation Systems ◽  
Phone Call ◽  
Discovery Process ◽  
Mobility Data ◽  
Formal Framework

The technologies of mobile communications and ubiquitous computing pervade society. Wireless networks sense the movement of people and vehicles, generating large volumes of mobility data, such as mobile phone call records and GPS tracks. This data can produce useful knowledge, supporting sustainable mobility and intelligent transportation systems, provided that a suitable knowledge discovery process is enacted for mining this mobility data. In this paper, the authors examine a formal framework, and the associated implementation, for a data mining query language for mobility data, created as a result of a European-wide research project called GeoPKDD (Geographic Privacy-Aware Knowledge Discovery and Delivery). The authors discuss how the system provides comprehensive support for the Mobility Knowledge Discovery process and illustrate its analytical power in unveiling the complexity of urban mobility in a large metropolitan area, based on a massive real life GPS dataset.

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