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Journal articles on the topic 'Hybrid data mining'

Author: Grafiati

Published: 11 September 2023

Last updated: 25 July 2025

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1

Elankavi, R., R. Kalaiprasath, and R. Udayakumar. "DATA MINING WITH BIG DATA REVOLUTION HYBRID." International Journal on Smart Sensing and Intelligent Systems 10, no. 4 (2017): 560–73. http://dx.doi.org/10.21307/ijssis-2017-270.

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2

Ambulkar, Bhagyashree, and Prof Gunjan Agre. "Data Mining Over Encrypted Data of Database Client Engine Using Hybrid Classification Approach." International Journal of Innovative Research in Computer Science & Technology 5, no. 3 (2017): 291–94. http://dx.doi.org/10.21276/ijircst.2017.5.3.7.

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3

Lakshmi Devasena, C., and M. Hemalatha. "A Hybrid Image Mining Technique using LIMbased Data Mining Algorithm." International Journal of Computer Applications 25, no. 2 (2011): 1–5. http://dx.doi.org/10.5120/3007-4056.

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4

Tamer, Uçar, and Karahoca Adem. "Benchmarking data mining approaches for traveler segmentation." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 1 (2021): 409–15. https://doi.org/10.11591/ijece.v11i1.pp409-415.

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The purpose of this study is proposing a hybrid data mining solution for traveler segmentation in tourism domain which can be used for planning user-oriented trips, arranging travel campaigns or similar services. Data set used in this work have been provided by a travel agency which contains flight and hotel bookings of travelers. Initially, the data set was prepared for running data mining algorithms. Then, various machine learning algorithms were benchmarked for performing accurate traveler segmentation and prediction tasks. Fuzzy C-means and X-means algorithms were applied for clustering us

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5

Shadroo, Shabnam, Mohsen Yoosefi Nejad, Samira Tavanaiee Yosefian, Morteza Naserbakht, and Mehdi Hosseinzadeh. "Proposing Two Hybrid Data Mining Models for Discovering Students' Mental Health Problems." Acta Informatica Pragensia 10, no. 1 (2021): 85–107. http://dx.doi.org/10.18267/j.aip.148.

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6

Harrag, Fouzi, and Ali Alshehri. "Applying Data Mining in Surveillance." International Journal of Distributed Systems and Technologies 14, no. 1 (2023): 1–24. http://dx.doi.org/10.4018/ijdst.317930.

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In the current times where human safety is threatened by man-made and natural calamities, surveillance systems have gained immense importance. But, even in presence of high definition (HD) security cameras and manpower to monitor the live feed 24/7, room for missing important information due to human error exists. In addition to that, employing an adequate number of people for the job is not always feasible either. The solution lies in a system that allows automated surveillance through classification and other data mining techniques that can be used for extraction of useful information out of

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7

Azad, Chandrashekhar. "Data Mining based Hybrid Intrusion Detection System." Indian Journal of Science and Technology 7, no. 6 (2014): 781–89. http://dx.doi.org/10.17485/ijst/2014/v7i6.19.

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8

Sharma, Monica, and Rajdeep Kaur. "Data Mining in Healthcare using Hybrid Approach." International Journal of Computer Applications 128, no. 4 (2015): 49–53. http://dx.doi.org/10.5120/ijca2015906539.

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Abidi, Balkis, Sadok Ben Yahia, and Charith Perera. "Hybrid microaggregation for privacy preserving data mining." Journal of Ambient Intelligence and Humanized Computing 11, no. 1 (2018): 23–38. http://dx.doi.org/10.1007/s12652-018-1122-7.

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10

Lee, Zne-Jung, Chou-Yuan Lee, So-Tsung Chou, Wei-Ping Ma, Fulan Ye, and Zhen Chen. "A hybrid system for imbalanced data mining." Microsystem Technologies 26, no. 9 (2019): 3043–47. http://dx.doi.org/10.1007/s00542-019-04566-1.

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Panda, Mrutyunjaya, and Ajith Abraham. "Hybrid evolutionary algorithms for classification data mining." Neural Computing and Applications 26, no. 3 (2014): 507–23. http://dx.doi.org/10.1007/s00521-014-1673-2.

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12

Srivastava, Ankit, Vijendra Singh, and Gurdeep Singh Drall. "Sentiment Analysis of Twitter Data." International Journal of Healthcare Information Systems and Informatics 14, no. 2 (2019): 1–16. http://dx.doi.org/10.4018/ijhisi.2019040101.

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Over the past few years, the novel appeal and increasing popularity of social networks as a medium for users to express their opinions and views have created an accumulation of a massive amount of data. This evolving mountain of data is commonly termed Big Data. Accordingly, one area in which the application of new techniques in data mining research has significant potential to achieve more precise classification of hidden knowledge in Big Data is sentiment analysis (aka optimal mining). A hybrid approach using Naïve Bayes and Random Forest on mining Twitter datasets is presented here as an ex

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13

Chen, Tung-Shou, Jeanne Chen, and Yuan-Hung Kao. "A Novel Hybrid Protection Technique of Privacy-Preserving Data Mining and Anti-Data Mining." Information Technology Journal 9, no. 3 (2010): 500–505. http://dx.doi.org/10.3923/itj.2010.500.505.

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14

Yi, Wenquan, Fei Teng, and Jianfeng Xu. "Noval Stream Data Mining Framework under the Background of Big Data." Cybernetics and Information Technologies 16, no. 5 (2016): 69–77. http://dx.doi.org/10.1515/cait-2016-0053.

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Abstract Stream data mining has been a hot topic for research in the data mining research area in recent years, as it has an extensive application prospect in big data ages. Research on stream data mining mainly focuses on frequent item sets mining, clustering and classification. However, traditional steam data mining methods are not effective enough for handling high dimensional data set because these methods are not fit for the characteristics of stream data. So, these traditional stream data mining methods need to be enhanced for big data applications. To resolve this issue, a hybrid framew

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15

Jashma, Suresh Ponmudiyan Poovan, Acharya Udupi Dinesh, and Veerappareddy Subba Reddy Nandanavana. "A multithreaded hybrid framework for mining frequent itemsets." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 3 (2022): 3249–64. https://doi.org/10.11591/ijece.v12i3.pp3249-3264.

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Mining frequent itemsets is an area of data mining that has beguiled several researchers in recent years. Varied data structures such as Nodesets, DiffNodesets, NegNodesets, N-lists, and Diffsets are among a few that were employed to extract frequent items. However, most of these approaches fell short either in respect of run time or memory. Hybrid frameworks were formulated to repress these issues that encompass the deployment of two or more data structures to facilitate effective mining of frequent itemsets. Such an approach aims to exploit the advantages of either of the data structures whi

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16

Pang, Lu. "Library Management System Based on Data Mining and Clustering Algorithm." Wireless Communications and Mobile Computing 2022 (September 2, 2022): 1–6. http://dx.doi.org/10.1155/2022/1398681.

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In order to solve the problem of building system services between readers and libraries, this paper proposes a library management system based on data mining and clustering algorithm. The library management model is built based on data mining technology and clustering algorithm, and the hybrid clustering algorithm in the data mining platform Weka is used for library data mining. The experimental results show that with the same amount of data, the hybrid clustering algorithm takes 5.5 seconds to process information from 0 to 300, which is at least 1 second faster than the other two algorithms.

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17

Kumar, Manish, Sumit Kumar, and Sweety. "Pattern Generation for Complex Data Using Hybrid Mining." International Journal of Data Mining & Knowledge Management Process 3, no. 4 (2013): 127–35. http://dx.doi.org/10.5121/ijdkp.2013.3409.

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18

Xhafa, Fatos, Francisco Herrera, and Mario Köppen. "Special issue: Data mining and hybrid intelligent systems." International Journal of Hybrid Intelligent Systems 6, no. 2 (2009): 67. http://dx.doi.org/10.3233/his-2009-0086.

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19

Lekhi, Nancy, and Manish Mahajan. "Outlier Reduction using Hybrid Approach in Data Mining." International Journal of Modern Education and Computer Science 7, no. 5 (2015): 43–49. http://dx.doi.org/10.5815/ijmecs.2015.05.06.

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20

Hudaib, Amjad, Reham Dannoun, Osama Harfoushi, Ruba Obiedat, and Hossam Faris. "Hybrid Data Mining Models for Predicting Customer Churn." International Journal of Communications, Network and System Sciences 08, no. 05 (2015): 91–96. http://dx.doi.org/10.4236/ijcns.2015.85012.

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21

kumar, Santhosh, and E. Ramaraj. "A Hybrid Model for Mining Multidimensional Data Sets." International Journal of Computer Applications Technology and Research 2, no. 3 (2013): 214–17. http://dx.doi.org/10.7753/ijcatr0203.1001.

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22

Mandala, Eka Praja Wiyata, Eva Rianti, and Sarjon Defit. "Classification of Customer Loans Using Hybrid Data Mining." JUITA: Jurnal Informatika 10, no. 1 (2022): 45. http://dx.doi.org/10.30595/juita.v10i1.12521.

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At this time, loans are one of the products offered by banks to their customers. BPR is an abbreviation of Bank Perkreditan Rakyat. BPR is one of the banks that provide loans to their customers. The problem that occurs is that the number of loans given to customers is often not on target and does not meet the criteria. We propose a hybrid data mining method which consists of two phases, first, we will cluster the eligibility of customers to be given a loan using the k-means algorithm, second, we will classify the loan amount using data from the clustering of eligible customers using k-nearest

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23

Nasir, Mahreen. "A Hybrid Data Mining Model for Intrusion Detection." International Journal of Computer Applications 183, no. 16 (2021): 14–19. http://dx.doi.org/10.5120/ijca2021921489.

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24

Patil, Rahul, Pavan Chopade, Abhishek Mishra, Bhushan Sane, and Yuvraj Sargar. "Disease Prediction System using Data Mining Hybrid Approach." Communications on Applied Electronics 4, no. 9 (2016): 48–51. http://dx.doi.org/10.5120/cae2016652154.

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25

Jun, Sung-Hae. "Ubiquitous Data Mining Using Hybrid Support Vector Machine." Journal of Korean Institute of Intelligent Systems 15, no. 3 (2005): 312–17. http://dx.doi.org/10.5391/jkiis.2005.15.3.312.

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26

QIAN, Chao, Hongke XU, Liang DAI, and Shuguang LI. "ETC Data Mining Based on Hybrid Markov Model." Journal of Transportation Systems Engineering and Information Technology 12, no. 4 (2012): 35–42. http://dx.doi.org/10.1016/s1570-6672(11)60212-2.

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27

Chen, Weimin, Guocheng Xiang, Youjin Liu, and Kexi Wang. "Credit risk Evaluation by hybrid data mining technique." Systems Engineering Procedia 3 (2012): 194–200. http://dx.doi.org/10.1016/j.sepro.2011.10.029.

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28

Barbalho, Hugo, Isabel Rosseti, Simone L. Martins, and Alexandre Plastino. "A hybrid data mining GRASP with path-relinking." Computers & Operations Research 40, no. 12 (2013): 3159–73. http://dx.doi.org/10.1016/j.cor.2012.02.022.

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29

Alizadeh, H., and Bidgoli B. Minaei. "Introducing A Hybrid Data Mining Model to Evaluate Customer Loyalty." Engineering, Technology & Applied Science Research 6, no. 6 (2016): 1235–40. https://doi.org/10.5281/zenodo.225499.

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The main aim of this study was introducing a comprehensive model of bank customers᾽ loyalty evaluation based on the assessment and comparison of different clustering methods᾽ performance. This study also pursues the following specific objectives: a) using different clustering methods and comparing them for customer classification, b) finding the effective variables in determining the customer loyalty, and c) using different collective classification methods to increase the modeling accuracy and comparing the results with the basic methods. Since loyal customers generate more profit, this study

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30

Uçar, Tamer, and Adem Karahoca. "Benchmarking data mining approaches for traveler segmentation." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 1 (2021): 409. http://dx.doi.org/10.11591/ijece.v11i1.pp409-415.

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The purpose of this study is proposing a hybrid data mining solution for traveler segmentation in tourism domain which can be used for planning user-oriented trips, arranging travel campaigns or similar services. Data set used in this work have been provided by a travel agency which contains flight and hotel bookings of travelers. Initially, the data set was prepared for running data mining algorithms. Then, various machine learning algorithms were benchmarked for performing accurate traveler segmentation and prediction tasks. Fuzzy C-means and X-means algorithms were applied for clustering us

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31

Naveen D. Surabhi, Srinivas, Chirag Vinalbhai Shah, Vishwanadham Mandala, and Priyank Shah. "Advancing Faux Image Detection: A Hybrid Approach Combining Deep Learning and Data Mining Techniques." International Journal of Science and Research (IJSR) 13, no. 3 (2024): 959–63. http://dx.doi.org/10.21275/sr24313094832.

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32

Sylvester Aondonenge, Dugguh, Ajayi Ore-Ofe, Kamorudeen Hassan Taiwo, et al. "Early Heart Disease Prediction Using Data Mining Techniques." Vokasi Unesa Bulletin of Engineering, Technology and Applied Science 2, no. 2 (2025): 211–26. https://doi.org/10.26740/vubeta.v2i2.36735.

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This study develops a predictive model for early heart disease detection using data mining techniques to enhance timely and accurate diagnosis. Heart disease prediction is complex due to the need to analyze various risk factors, such as age, cholesterol, and blood pressure. The model integrates multiple machines learning algorithms, including Random Forest, Support Vector Machine, and a hybrid ensemble approach, aiming to achieve higher prediction accuracy and reliability. The methodology follows five phases which include data collection, data pre-processing, feature extraction, model construc

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33

Bakri, Rizal, Niken Probondani Astuti, and Ansari Saleh Ahmar. "Evaluating Random Forest Algorithm in Educational Data Mining: Optimizing Graduation on-time prediction using Imbalance Methods." ARRUS Journal of Social Sciences and Humanities 4, no. 1 (2024): 108–16. http://dx.doi.org/10.35877/soshum2449.

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The study aims to evaluate the performance of Random Forest algorithms in data mining education by optimizing graduation on-time (GOT) predictions using imbalanced data methods. Methods used to handle imbalanced data include random under-sampling (RUS), random over-sampling (ROS), hybrids of RUS and ROS, synthetic minority over-sampling techniques for nominal classes (SMOTE-NC), and hybrids of SMOTE-NC and RUS. After applying these methods, studies analyze their performance on training and testing data. The research findings show that on training data, the RUS-ROS hybrid showed the best perfor

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34

Ali, Hameed Yassir, A. Mohammed Ali, Abdul-Jabbar Alkhazraji Adel, Emad Hameed Mustafa, Saad Talib Mohammed, and Faeq Ali Mohanad. "Sentimental classification analysis of polarity multi-view textual data using data mining techniques." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 5 (2020): 5526–34. https://doi.org/10.11591/ijece.v10i5.pp5526-5534.

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The data and information available in most community environments is complex in nature. Sentimental data resources may possibly consist of textual data collected from multiple information sources with different representations and usually handled by different analytical models. These types of data resource characteristics can form multi-view polarity textual data. However, knowledge creation from this type of sentimental textual data requires considerable analytical efforts and capabilities. In particular, data mining practices can provide exceptional results in handling textual data formats.

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35

Jamalian, Elham, and Rahim Foukerdi. "A Hybrid Data Mining Method for Customer Churn Prediction." Engineering, Technology & Applied Science Research 8, no. 3 (2018): 2991–97. https://doi.org/10.5281/zenodo.1400547.

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The expenses for attracting new customers are much higher compared to the ones needed to maintain old customers due to the increasing competition and business saturation. So customer retention is one of the leading factors in companies’ marketing. Customer retention requires a churn management, and an effective management requires an exact and effective model for churn prediction. A variety of techniques and methodologies have been used for churn prediction, such as logistic regression, neural networks, genetic algorithm, decision tree etc.. In this article, a hybrid method is presented

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36

AlJanabi, Kadhim B. S., and Rusul Kadhim Meshjal. "A Hybrid Data Warehouse Model to Improve Mining Algorithms." Journal of Kufa for Mathematics and Computer 4, no. 3 (2017): 21–30. http://dx.doi.org/10.31642/jokmc/2018/040304.

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The performance of different Data Mining Algorithms including Classification, Clustering, Association, Prediction and others are highly related to the approaches used in Data Warehouse design and to the way the data is stored (lightly summarized, highly summarized and detailed).Detailed data is important to get detailed reports but as the amount of data is huge this represents a big challenge to the mining algorithms, on the other hand, the summarized data leads to better algorithms performance but the lack of the required knowledge may affect the overall mining process.  Knowledge extra

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37

Mandapati, Sridhar, Raveendra Babu Bhogapathi, and Ratna Babu Chekka. "A Hybrid Algorithm for Privacy Preserving in Data Mining." International Journal of Intelligent Systems and Applications 5, no. 8 (2013): 47–53. http://dx.doi.org/10.5815/ijisa.2013.08.06.

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38

Panda, Mrutyunjaya, Aboul Ella Hassanien, and Ajith Abraham. "Hybrid Data Mining Approach for Image Segmentation Based Classification." International Journal of Rough Sets and Data Analysis 3, no. 2 (2016): 65–81. http://dx.doi.org/10.4018/ijrsda.2016040105.

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Evolutionary harmony search algorithm is used for its capability in finding solution space both locally and globally. In contrast, Wavelet based feature selection, for its ability to provide localized frequency information about a function of a signal, makes it a promising one for efficient classification. Research in this direction states that wavelet based neural network may be trapped to fall in a local minima whereas fuzzy harmony search based algorithm effectively addresses that problem and able to get a near optimal solution. In this, a hybrid wavelet based radial basis function (RBF) ne

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39

Mohammed, Faris E., Nadia Smaoui Zghal, Dalinda Ben Aissa, and Mostafa Mahmoud El-Gayar. "Classify Breast Cancer Patients using Hybrid Data-Mining Techniques." Journal of Computer Science 18, no. 4 (2022): 316–21. http://dx.doi.org/10.3844/jcssp.2022.316.321.

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40

Abedini, Mohammadali, Farzaneh Ahmadzadeh, and Rassoul Noorossana. "Customer credit scoring using a hybrid data mining approach." Kybernetes 45, no. 10 (2016): 1576–88. http://dx.doi.org/10.1108/k-09-2015-0228.

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Purpose A crucial decision in financial services is how to classify credit or loan applicants into good and bad applicants. The purpose of this paper is to propose a four-stage hybrid data mining approach to support the decision-making process. Design/methodology/approach The approach is inspired by the bagging ensemble learning method and proposes a new voting method, namely two-level majority voting in the last stage. First some training subsets are generated. Then some different base classifiers are tuned and afterward some ensemble methods are applied to strengthen tuned classifiers. Final

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41

Nega, Adane, and Alemu Kumlachew. "Data Mining Based Hybrid Intelligent System for Medical Application." International Journal of Information Engineering and Electronic Business 9, no. 4 (2017): 38–46. http://dx.doi.org/10.5815/ijieeb.2017.04.06.

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42

Jamalian, E., and R. Foukerdi. "A Hybrid Data Mining Method for Customer Churn Prediction." Engineering, Technology & Applied Science Research 8, no. 3 (2018): 2991–97. http://dx.doi.org/10.48084/etasr.2108.

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The expenses for attracting new customers are much higher compared to the ones needed to maintain old customers due to the increasing competition and business saturation. So customer retention is one of the leading factors in companies’ marketing. Customer retention requires a churn management, and an effective management requires an exact and effective model for churn prediction. A variety of techniques and methodologies have been used for churn prediction, such as logistic regression, neural networks, genetic algorithm, decision tree etc.. In this article, a hybrid method is presented that p

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43

Yang, Lechan, Zhihao Qin, Kun Wang, and Song Deng. "Hybrid gene expression programming-based sensor data correlation mining." China Communications 14, no. 1 (2017): 34–49. http://dx.doi.org/10.1109/cc.2017.7839756.

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44

Mehla, Stuti, and Sanjeev Rana. "A Hybrid Approach for Opinion Mining Using Twitter Data." Journal of Computational and Theoretical Nanoscience 16, no. 9 (2019): 3817–23. http://dx.doi.org/10.1166/jctn.2019.8255.

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Abrupt and fast change of technology give birth to research areas which are directly related to users. NLP is such type of emerging research area in which opinions of users using technology play the important role. As with advancement in social sites people post every information. These posts are related to perspective of user about some company service, regarding any political party and review related to entertainment industry. In NLP, opinion mining is considered as important field because it directly related to society opinion. It can be described as that field in which conclusion of posted

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45

Chu, Bong-Horng, Ming-Shian Tsai, and Cheng-Seen Ho. "Toward a hybrid data mining model for customer retention." Knowledge-Based Systems 20, no. 8 (2007): 703–18. http://dx.doi.org/10.1016/j.knosys.2006.10.003.

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46

Vatsavai, Ranga Raju, and Budhendra Bhaduri. "A hybrid classification scheme for mining multisource geospatial data." GeoInformatica 15, no. 1 (2010): 29–47. http://dx.doi.org/10.1007/s10707-010-0113-4.

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47

Kotsiantis, S. "Credit risk analysis using a hybrid data mining model." International Journal of Intelligent Systems Technologies and Applications 2, no. 4 (2007): 345. http://dx.doi.org/10.1504/ijista.2007.014030.

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48

i, stutiii, Shashwat Tandon, Manjula R, and Shiv Kumar. "A Hybrid Approach of Weather Forecasting using Data Mining." International Research Journal on Advanced Science Hub 5, Issue 05S (2023): 219–28. http://dx.doi.org/10.47392/irjash.2023.s029.

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49

Lang, Susan, and Craig Baehr. "Data Mining: A Hybrid Methodology for Complex and Dynamic Research." College Composition & Communication 64, no. 1 (2012): 172–94. http://dx.doi.org/10.58680/ccc201220865.

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This article provides an overview of the ways in which data and text mining have potentialas research methodologies in composition studies. It introduces data mining in thecontext of the field of composition studies and discusses ways in which this methodologycan complement and extend our existing research practices by blending the best of whattechnology and researchers have to offer. The authors examine a process model for datamining, discuss benefits and liabilities, and link to increased calls for accountability.

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50

Dr. T. Senthil Kumar. "Data Mining Based Marketing Decision Support System Using Hybrid Machine Learning Algorithm." September 2020 2, no. 3 (2020): 185–93. http://dx.doi.org/10.36548//jaicn.2020.3.006.

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Data mining is widely used in engineering and science, On the contrary, it is used in finance and marketing applications to resolve the challenges in the respective fields. Data mining based decision support system enhances the organization performance by analysing the ground reality. Turbulent economy is common for every organization due to the competition, cost, tax pressures, etc., Privatization, Globalization and liberalization drags the organization more into a competitive environment. In order to balance the competition and withstand to achieve desired gain proper marketing strategies ar

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