scholarly journals Telemarketing Guidance in Selling Banking Services: A Data Mining Approach

2021 ◽  
Vol 1 (1) ◽  
pp. 1-16
Author(s):  
Kattareeya Prompreing ◽  
Theera Prompreing
Keyword(s):  
Data Mining ◽  
Information Gain ◽  
Data Mining Technique ◽  
Attribute Importance ◽  
Managerial Implication ◽  
Banking Services ◽  
Data Mining Approach ◽  
Information Gain Ratio ◽  
Potential Customers ◽  
Fold Cross Validation

In telemarketing activity, selecting the most potential customers are important because can reduce processing time and operational cost. Therefore, the ability to select the most likely buying customers are urgently needed. In this study, we propose a clear sequence in doing telemarketing activity based on the previous telemarketing data which applying data mining technique. We weight the importance of 16 customer characteristics through 45,211 observations from a Portuguese bank. Applying Random Forest algorithm along with Information Gain Ratio as a criterion and 10-fold Cross Validation, the model able to weight the importance of attributes and achieves 90.01 % accuracy in predicting telemarketing success. Furthermore, the rank of attribute importance was designed to be a guidance map in selecting potential targeted customers as a managerial implication.

Test Case Reduction Using Data Mining Technique

2016 ◽  
Vol 4 (4) ◽  
pp. 56-70 ◽  
Author(s):  
Ahmad A. Saifan ◽  
Emad Alsukhni ◽  
Hanadi Alawneh ◽  
Ayat AL Sbaih
Keyword(s):  
Data Mining ◽  
Clustering Algorithm ◽  
Test Case ◽  
Test Cases ◽  
Data Mining Technique ◽  
Code Coverage ◽  
Test Case Selection ◽  
Data Mining Approach ◽  
Case Selection ◽  
Using Data

Software testing is a process of ratifying the functionality of software. It is a crucial area which consumes a great deal of time and cost. The time spent on testing is mainly concerned with testing large numbers of unreliable test cases. The authors' goal is to reduce the numbers and offer more reliable test cases, which can be achieved using certain selection techniques to choose a subset of existing test cases. The main goal of test case selection is to identify a subset of the test cases that are capable of satisfying the requirements as well as exposing most of the existing faults. The state of practice among test case selection heuristics is cyclomatic complexity and code coverage. The authors used clustering algorithm which is a data mining approach to reduce the number of test cases. Their approach was able to obtain 93 unique effective test cases out a total of 504.

A data mining approach for turning potential customers into real ones in basket purchase analysis

2015 ◽  
Vol 19 (2) ◽  
pp. 139 ◽  
Author(s):  
Abolfazl Kazemi ◽  
Mohammad Esmaeil Babaei ◽  
Mahsa Oroojeni Mohammad Javad
Keyword(s):  
Data Mining ◽  
Data Mining Approach ◽  
Potential Customers

An Improved SPRINT Algorithm

2012 ◽  
Vol 532-533 ◽  
pp. 1685-1690 ◽  
Author(s):  
Zhi Kang Luo ◽  
Huai Ying Sun ◽  
De Wang
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Decision Tree ◽  
Learning Communities ◽  
Information Gain ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
Gain Ratio ◽  
Information Gain Ratio ◽  
Improved Algorithm

This paper presents an improved SPRINT algorithm. The original SPRINT algorithm is a scalable and parallelizable decision tree algorithm, which is a popular algorithm in data mining and machine learning communities. To improve the algorithm's efficiency, we propose an improved algorithm. Firstly, we select the splitting attributes and obtain the best splitting attribute from them by computing the information gain ratio of each attribute. After that, we calculate the best splitting point of the best splitting attribute. Since it avoids a lot of calculations of other attributes, the improved algorithm can effectively reduce the computation.

scholarly journals Comparative Study of Classification Method on Customer Candidate Data to Predict its Potential Risk

2018 ◽  
Vol 8 (6) ◽  
pp. 4763 ◽  
Author(s):  
Mujiono Sadikin ◽  
Fahri Alfiandi
Keyword(s):  
Data Mining ◽  
Naive Bayes ◽  
Evaluation Criteria ◽  
Naïve Bayes ◽  
Data Mining Technique ◽  
Future Behavior ◽  
Validation Experiment ◽  
C4.5 Algorithm ◽  
A Company ◽  
Potential Customers

Leasing vehicles are a company engaged in the field of vehicle loans. Purchase by way of credit becomes a mainstay because it can attract potential customers to generate more profit. But if there is a mistake in approving a customer candidate, the risk of stalled credit payments can happen. To minimize the risk, it can be applied the certain data mining technique to predict the future behavior of the customers. In this study, it is explored in some data mining techniques such as C4.5 and Naive Bayes for this purpose. The customer attributes used in this study are: salary, age, marital status, other installments and worthiness. The experiments are performed by using the Weka software. Based on evaluation criteria, i.e. accuracy, C4.5 algorithm outperforms compared to Naive Bayes. The percentage split experiment scenarios provide the precision value of 89.16% and the accuracy value of 83.33% wheres the cross validation experiment scenarios give the higher accuracy values of all used k-fold. The C4.5 experiment results also confirm that the most influential instant data attribute in this research is the salary.

scholarly journals Identification of important features and data mining classification techniques in predicting employee absenteeism at work

2021 ◽  
Vol 11 (5) ◽  
pp. 4587
Author(s):  
Amal Al-Rasheed
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
Prediction Models ◽  
Information Gain ◽  
Feature Selection Method ◽  
Performance Measurements ◽  
Data Mining Technique ◽  
Classification Techniques ◽  
Mining Technique ◽  
Correlation Based Feature Selection

Employees absenteeism at the work costs organizations billions a year. Prediction of employees’ absenteeism and the reasons behind their absence help organizations in reducing expenses and increasing productivity. Data mining turns the vast volume of human resources data into information that can help in decision-making and prediction. Although the selection of features is a critical step in data mining to enhance the efficiency of the final prediction, it is not yet known which method of feature selection is better. Therefore, this paper aims to compare the performance of three well-known feature selection methods in absenteeism prediction, which are relief-based feature selection, correlation-based feature selection and information-gain feature selection. In addition, this paper aims to find the best combination of feature selection method and data mining technique in enhancing the absenteeism prediction accuracy. Seven classification techniques were used as the prediction model. Additionally, cross-validation approach was utilized to assess the applied prediction models to have more realistic and reliable results. The used dataset was built at a courier company in Brazil with records of absenteeism at work. Regarding experimental results, correlationbased feature selection surpasses the other methods through the performance measurements. Furthermore, bagging classifier was the best-performing data mining technique when features were selected using correlation-based feature selection with an accuracy rate of (92%).

scholarly journals Leveraging Social Media Metrics in Improving Social Media Performances through Organic Reach: A Data Mining Approach

2018 ◽  
Vol 11 (2) ◽  
pp. 33-48
Author(s):  
R. Jen-peng Huang ◽  
Genesis Sembiring Depari ◽  
Sri Vandayuli Riorini ◽  
Pai-Chou Wang
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Support Vector Machines ◽  
The Other ◽  
Support Vector ◽  
Managerial Implication ◽  
Potential Models ◽  
Data Mining Approach ◽  
Vector Machines

AbstractThis paper identified the relevance of several publication’s characteristics of each publication in reaching more people through organic strategy using Support Vector Machines. Before finding the relevance of several inputs, 10 potential models were examined. Based on the results of 10 models examination, we found that Comments, Likes and Shares have smallest error. However, those variables represent the customer engagements, instead of reaching more people. In the other side, Lifetime total organic reach is the best model compares to other models, therefore lifetime total organic reach was selected as a model. Furthermore, page total likes were found as the most relevance input in reaching more people through organic reach. The next most relevance inputs were followed by Type, month, day and hour of publication. Eventually, we come up with a managerial implication on how to publish a post in order to reach more people through organic reach.

scholarly journals Data Mining Approach for Educational Decision Support

2021 ◽  
Vol 2 (1) ◽  
pp. 33-44
Author(s):  
Sinta Septi Pangastuti ◽  
Kartika Fithriasari ◽  
Nur Iriawan ◽  
Wahyuni Suryaningtyas
Keyword(s):  
Data Mining ◽  
Random Forest ◽  
Classification Accuracy ◽  
Storage System ◽  
Performance Criteria ◽  
Data Mining Technique ◽  
Mining Technique ◽  
Data Mining Approach ◽  
Educational Decision ◽  
Boosting Algorithm

data mining techniques in education sector have begun to evolve, along with the development of technology and the amount of data that can be stored in an education database storage system. One of them is a database of Bidikmisi scholarships in Indonesia. The Bidikmisi data used in this study will be classified using classification data mining technique. The technique that used in this study is random forest in combination with boosting algorithm and bagging algorithms. These algorithms also combine with SMOTE algorithm to handling the imbalance class in dataset. Based on the performance criteria G-mean and AUC, the algorithm combines with SMOTE tended to be better. The classification accuracy of each method being more than 90%

scholarly journals Aplikasi Algoritma CBA untuk Klasifikasi Resiko Pemberian Kredit (Studi kasus: PT. Telkom CDC Sub Area Kupang)

2014 ◽  
Vol 8 (2) ◽  
pp. 121
Author(s):  
Robynson Amseke ◽  
Edi Winarko
Keyword(s):  
Data Mining ◽  
Cross Validation ◽  
Subjective Evaluation ◽  
Computer Application ◽  
Data Mining Technique ◽  
Mining Technique ◽  
Testing Techniques ◽  
Credit Data ◽  
Fold Cross Validation

AbstrakSalah satu penyebab kredit bermasalahberasal dari pihak internal, yaitu kurang telitinya timdalam melakukan survei dan analisis, atau bisa juga karena penilaian dan analisis yang bersifat subjektif.Penyebab ini dapat diatasi dengan sistem komputer, yaitu aplikasi komputer yang menggunakan teknik data mining.Teknik data mining digunakan dalam penelitian ini untuk klasifikasi resiko pemberian kredit dengan menerapkan algoritma Classification Based On Association (CBA). Algoritma ini merupakan salah satu algoritma klasifikasi dalam data mining yang mengintegrasikan teknik asosiasi dan klasifikasi. Data kredit awal yang telah di-preprocessing, diproses menggunakan algoritma CBA untuk membangun model, lalu model tersebut digunakan untuk mengklasifikasi data pelaku usaha baru yang mengajukan kredit ke dalam kelas lancar atau macet.Teknik Pengujian akurasi model diukur menggunakan 10-fold cross validation. Hasil pengujian menunjukkan bahwa rata-rata nilai akurasi menggunakan algoritma CBA (57,86%), sedikit lebih tinggi dibandingkan rata-rata nilai akurasi menggunakan algoritma Naive Bayes dan SVM dari perangkat lunak Rapid Miner 5.3 (56,35% dan 55,03%). Kata kunci—classification based on association, CBA, data mining, klasifikasi, resiko pemberian kredit  AbstractOne of the causes of non-performing loans come from the internal, that is caused by a lack of rigorous team in conducting the survey and analysis, or it could be due to subjective evaluation and analysis. The cause of this can be solved by a computer system, the computer application that uses data mining techniques. Data mining technique, was usedin this study toclassifycreditriskby applyingalgorithmsClassificationBasedonAssociation(CBA). This algorithm is an algorithm classification of data mining which integratingassociationandclassificationtechniques. Preprocessed initial-credit data, will be processed using theCBAalgorithmto create a model of which is toclassifythe newloandata into swift class or bad one. Testing techniques the accuracy of the model was measured by 10-fold cross validation. The resultshowsthatthe accuracy averagevalue using theCBAalgorithm(57,86%), was slightly higher than those using thealgorithmsofSVM andNaiveBayes from RapidMiner5.3software(56,35% and55,03%, respectively). Keywords—classification based on association, CBA, data mining, classification, credit risk 

scholarly journals Data Mining Approach in Preterm Birth Prediction

2010 ◽  
Vol 9 (1) ◽  
pp. 18-30 ◽  
Author(s):  
Jyothi Thomas ◽  
G. Kulanthaivel
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Neural Networks ◽  
Preterm Birth ◽  
Field Data ◽  
Risk Groups ◽  
Data Mining Technique ◽  
Mining Technique ◽  
Data Mining Approach ◽  
Health Field

Data mining refers to the process of discovering patterns in data, typically with the aid of powerful algorithms to automate part of the search. These methods come from the disciplines such as statistics, machine learning, pattern recognition, neural networks and database. In particular this paper reveals out how the problem of preterm birth prediction is approached by a data mining analyst with a background in machine learning. In the health field, data mining applications have been growing considerably as it can be used to directly derive patterns, which are relevant to forecast different risk groups among the patients. Data mining technique such as clustering has not been used to predict preterm birth. Hence this paper made an attempt to identify patterns from the database of the preterm birth patients using clustering.

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