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.

A hybrid model using data mining and multi-criteria decision-making methods for landslide risk mapping at Golestan Province, Iran

2021 ◽  
Vol 80 (15) ◽  
Author(s):  
Elham Rafiei Sardooi ◽  
Ali Azareh ◽  
Tayyebeh Mesbahzadeh ◽  
Farshad Soleimani Sardoo ◽  
Eric J. R. Parteli ◽  
...  
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Hybrid Model ◽  
Risk Mapping ◽  
Landslide Risk ◽  
Multi Criteria Decision Making ◽  
Golestan Province ◽  
Using Data

Optimizing and Managing Digital Telecommunication Systems Using Data Mining and Knowledge Discovery Approaches

2011 ◽  
pp. 2360-2379
Author(s):  
Adnan I. Al Rabea ◽  
Ibrahiem M. M. El Emary
Keyword(s):  
Data Mining ◽  
Quality Of Service ◽  
Knowledge Discovery ◽  
Self Organizing Map ◽  
Performance Measurements ◽  
Operating Personnel ◽  
Telecommunication Systems ◽  
Original Measurement ◽  
Using Data

This chapter is interested in discussing and reporting how one can be benefited by using Data Mining and Knowledge Discovery techniques in achieving an acceptable level of quality of service of telecommunication systems. The quality of service is defined as the metrics which is predicated by using the data mining techniques, decision tree, association rules and neural networks. Digital telecommunication networks are highly complex systems and thus their planning, management and optimization are challenging tasks. The user expectations constitute the Quality of Service (QoS). To gain a competitive edge on other operators, the operating personnel have to measure the network in terms of QoS. In current times, there are three data mining methods applied to actual GSM network performance measurements, in which the methods were chosen to help the operating staff to find the essential information in network quality performance measurements. The results of Pekko (2004) show that the analyst can make good use of Rough Sets and Classification and Regression Trees (CART), because their information can be expressed in plain language rules that preserve the variable names of the original measurement. In addition, the CART and the Self-Organizing Map (SOM) provide effective visual means for interpreting the data set.

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.

Optimizing and Managing Digital Telecommunication Systems Using Data Mining and Knowledge Discovery Approaches

2010 ◽  
pp. 66-84
Author(s):  
Adnan I. Al Rabea ◽  
Ibrahiem M. M. El Emary
Keyword(s):  
Data Mining ◽  
Quality Of Service ◽  
Knowledge Discovery ◽  
Self Organizing Map ◽  
Performance Measurements ◽  
Operating Personnel ◽  
Telecommunication Systems ◽  
Original Measurement ◽  
Using Data

This chapter is interested in discussing and reporting how one can be benefited by using Data Mining and Knowledge Discovery techniques in achieving an acceptable level of quality of service of telecommunication systems. The quality of service is defined as the metrics which is predicated by using the data mining techniques, decision tree, association rules and neural networks. Digital telecommunication networks are highly complex systems and thus their planning, management and optimization are challenging tasks. The user expectations constitute the Quality of Service (QoS). To gain a competitive edge on other operators, the operating personnel have to measure the network in terms of QoS. In current times, there are three data mining methods applied to actual GSM network performance measurements, in which the methods were chosen to help the operating staff to find the essential information in network quality performance measurements. The results of Pekko (2004) show that the analyst can make good use of Rough Sets and Classification and Regression Trees (CART), because their information can be expressed in plain language rules that preserve the variable names of the original measurement. In addition, the CART and the Self-Organizing Map (SOM) provide effective visual means for interpreting the data set.

A New Associative Classification Algorithm for Predicting Groundwater Locations

2018 ◽  
Vol 17 (04) ◽  
pp. 1850043
Author(s):  
Faisal Aburub ◽  
Wa’el Hadi
Keyword(s):  
Data Mining ◽  
Groundwater Exploration ◽  
Classification Algorithms ◽  
Associative Classification ◽  
Detection Problem ◽  
Classification Rate ◽  
Data Mining Algorithms ◽  
New Locations ◽  
Using Data ◽  
Mining Algorithms

In this paper, we study the problem of predicting new locations of groundwater in Jordan through the application of a proposed new method, Groundwater Prediction using Associative Classification (GwPAC). We identify features that differentiate locations of groundwater wells according to whether or not they contain water. In addition, we survey intelligent-based methods related to groundwater exploration and management. Three experimental analyses were conducted with the objective to evaluate the capability of data mining algorithms using real groundwater data from the Ministry of Water and Irrigation. In the first experiment, we investigated the performance of GwPAC against three well-known associative classification algorithms, namely CBA, CMAR and FACA. Furthermore, three rule-based algorithms — C4.5, Random Forest and PBC4cip — were investigated in the second experiment; further, so as to generalise the capability of using data mining for solving the groundwater detection problem, four benchmark algorithms — SVMs, NB, KNN and ANNs — were evaluated in the third experiment. From all the experiments, the results indicated that all considered data mining algorithms predict locations of groundwater with acceptable classification rate (all classification accuracies [Formula: see text]%), and can be useful methods when seeking to address the problem of exploring new groundwater locations.

Vertical Fragmentation in Databases Using Data-Mining Technique

2009 ◽  
pp. 2543-2563 ◽  
Author(s):  
Narasimhaiah Gorla ◽  
Pang Wing Yan Betty
Keyword(s):  
Data Mining ◽  
Relational Databases ◽  
Real Life ◽  
Optimal Solution ◽  
Database Systems ◽  
Future Research ◽  
Data Mining Technique ◽  
Mining Technique ◽  
Vertical Fragmentation ◽  
Using Data

A new approach to vertical fragmentation in relational databases is proposed using association rules, a data-mining technique. Vertical fragmentation can enhance the performance of database systems by reducing the number of disk accesses needed by transactions. By adapting Apriori algorithm, a design methodology for vertical partitioning is proposed. The heuristic methodology is tested using two real-life databases for various minimum support levels and minimum confidence levels. In the smaller database, the partitioning solution obtained matched the optimal solution using exhaustive enumeration. The application of our method on the larger database resulted in the partitioning solution that has an improvement of 41.05% over unpartitioned solution and took less than a second to produce the solution. We provide future research directions on extending the procedure to distributed and object-oriented database designs.

Using Data Analytics Results in Practice: Challenges and Solution Directions

2021 ◽  
Author(s):  
Sunil Choenni ◽  
Mortaza S. Bargh ◽  
Niels Netten ◽  
Susan Van Den Braak
Keyword(s):  
Data Mining ◽  
Social Services ◽  
Data Analytics ◽  
Business Processes ◽  
Real Life ◽  
Other Information ◽  
Harvest Data ◽  
Using Data ◽  
Over Time ◽  
Combined Data

Organizations collect a vast amount of data of different types, from various sources, and through different channels. Primarily, these data are used by these organizations to facilitate their core business processes. However, today we witness a growing tendency to use these data for other purposes than that they are collected for. To this end, the data from one information system are combined with those of other information systems. Subsequently, the combined data are analyzed with advanced data analytics tools. Although there is a strong and practical need to apply such findings of data analytics to improve, among others, organizations’ (social) services, it is often not straightforward how to apply these findings in practice. This is due to many challenges arising from legal, ethical, and data quality concerns. In this chapter, we discuss the main reasons that hamper the application of data analytics findings, particularly pertaining to data collection processes and data analysis processes (like data mining and statistics). These reasons include inadequate transformations of statistical truths to individual cases, chances to fall into the trap of system realities, and required efforts to deal with the evolving semantics of data over time. The latter is due to the fact that our (social) environment is subjected to constant changes. We discuss two strategies to harvest data analytics findings in a responsible way. By means of some real-life examples in the field of social services we illustrate the applications of the strategies in practice. Furthermore, we argue that the findings from data-driven analytics may augment real-world ecosystems if they are applied with caution and responsibly.

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