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Journal articles on the topic 'Hybrid data mining'

Author: Grafiati

Published: 11 September 2023

Last updated: 8 June 2024

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1

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 (May 31, 2017): 291–94. http://dx.doi.org/10.21276/ijircst.2017.5.3.7.

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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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Lakshmi Devasena, C., and M. Hemalatha. "A Hybrid Image Mining Technique using LIMbased Data Mining Algorithm." International Journal of Computer Applications 25, no. 2 (July 31, 2011): 1–5. http://dx.doi.org/10.5120/3007-4056.

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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 (June 30, 2021): 85–107. http://dx.doi.org/10.18267/j.aip.148.

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5

Azad, Chandrashekhar. "Data Mining based Hybrid Intrusion Detection System." Indian Journal of Science and Technology 7, no. 6 (June 20, 2014): 781–89. http://dx.doi.org/10.17485/ijst/2014/v7i6.19.

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Sharma, Monica, and Rajdeep Kaur. "Data Mining in Healthcare using Hybrid Approach." International Journal of Computer Applications 128, no. 4 (October 15, 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 (November 26, 2018): 23–38. http://dx.doi.org/10.1007/s12652-018-1122-7.

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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 (August 8, 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 (August 10, 2014): 507–23. http://dx.doi.org/10.1007/s00521-014-1673-2.

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Harrag, Fouzi, and Ali Alshehri. "Applying Data Mining in Surveillance." International Journal of Distributed Systems and Technologies 14, no. 1 (February 10, 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 these inputs. In this research, a data mining-based framework has been proposed for surveillance. The research includes interpretation of data from different networks using hybrid data mining technique. In order to show the validity of the proposed hybrid data mining technique, an online data set containing network of a suspicious group has been utilized and main leaders of network has been identified.

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Srivastava, Ankit, Vijendra Singh, and Gurdeep Singh Drall. "Sentiment Analysis of Twitter Data." International Journal of Healthcare Information Systems and Informatics 14, no. 2 (April 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 extension of previous work. Briefly, relevant data sets are collected from Twitter using Twitter API; then, use of the hybrid methodology is illustrated and evaluated against one with only Naïve Bayes classifier. Results show better accuracy and efficiency in the sentiment classification for the hybrid approach.

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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 (March 15, 2010): 500–505. http://dx.doi.org/10.3923/itj.2010.500.505.

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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 (October 1, 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 framework is proposed for big steam data mining. In this framework, online and offline model are organized for different tasks, the interior of each model is rationally organized according to different mining tasks. This framework provides a new research idea and macro perspective for stream data mining under the background of big data.

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14

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 (July 31, 2013): 127–35. http://dx.doi.org/10.5121/ijdkp.2013.3409.

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Xhafa, Fatos, Francisco Herrera, and Mario Köppen. "Special issue: Data mining and hybrid intelligent systems." International Journal of Hybrid Intelligent Systems 6, no. 2 (May 7, 2009): 67. http://dx.doi.org/10.3233/his-2009-0086.

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Lekhi, Nancy, and Manish Mahajan. "Outlier Reduction using Hybrid Approach in Data Mining." International Journal of Modern Education and Computer Science 7, no. 5 (May 8, 2015): 43–49. http://dx.doi.org/10.5815/ijmecs.2015.05.06.

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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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kumar, Santhosh, and E. Ramaraj. "A Hybrid Model for Mining Multidimensional Data Sets." International Journal of Computer Applications Technology and Research 2, no. 3 (May 1, 2013): 214–17. http://dx.doi.org/10.7753/ijcatr0203.1001.

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Mandala, Eka Praja Wiyata, Eva Rianti, and Sarjon Defit. "Classification of Customer Loans Using Hybrid Data Mining." JUITA: Jurnal Informatika 10, no. 1 (May 27, 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 neighbors. As a result of this study, we were able to cluster 25 customers into 2 clusters, 10 customers into the "Not Feasible" cluster, 15 customers into the "Feasible" cluster. Then we also succeeded in classifying customers who applied for new loans with occupation is Entrepreneur, salary is ≥ IDR 5000000, loan guarantees Proof of Vehicle Owner, account balance is < IDR 5000000 and family members is ≥ 4. And the results, classified as Loans with a small amount. We obtained the level of validity of the data testing of each input variable to the target variable reached 97.57%.

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

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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 (April 26, 2016): 48–51. http://dx.doi.org/10.5120/cae2016652154.

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Jun, Sung-Hae. "Ubiquitous Data Mining Using Hybrid Support Vector Machine." Journal of Korean Institute of Intelligent Systems 15, no. 3 (June 1, 2005): 312–17. http://dx.doi.org/10.5391/jkiis.2005.15.3.312.

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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 (August 2012): 35–42. http://dx.doi.org/10.1016/s1570-6672(11)60212-2.

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

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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 (March 5, 2024): 959–63. http://dx.doi.org/10.21275/sr24313094832.

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27

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. Conclusion. The algorithm is not only a means of library system automation management, but also an effective means to realize library information modernization.

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Uçar, Tamer, and Adem Karahoca. "Benchmarking data mining approaches for traveler segmentation." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 1 (February 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 user data. J48 and multilayer perceptron (MLP) algorithms were applied for classifying instances based on segmented user data. According to the findings of this study, J48 has the most effective classification results when applied on the data set which is clustered with X-means algorithm. The proposed hybrid data mining solution can be used by travel agencies to plan trip campaigns for similar travelers.

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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 (February 27, 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 performance compared to other methods, while the SMOTENC and RUS hybrid techniques showed the best performance on testing data based on AUC values. The research showed that the use of an imbalanced data method significantly improved the ability of Random Forest algorithms to predict graduation on time (GOT) in the context of educational data. We discuss the implications for educational data mining applications and provide suggestions for future research.

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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 (December 30, 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 extraction and mining algorithms performance and complexities represent a big challenge in data analysis field, hence the work in this paper represents a proposed approach to improve the algorithms performance throughout well designed warehouse and data reduction technique. The work in this paper presents a hybrid warehouse galaxy model that stores data in three different formats including detailed, summarized and highly summarized data. The time and space complexity are the major criteria in the proposed approach. Real data was collected about schools, students and teachers from different AlNajaf AlAshraf cities, the data was preprocessed, reduced mainly through concept hierarchy and then converted into dimensions and fact tables (Warehouse Galaxy Model) which in turn are converted into multidimensional cubes. Roll up and drill down queries were highly used to get the required information. The resultant data cubes and in turn the corresponding warehouse model presented in this work showed a reasonable improvement in knowledge extraction algorithms for the data under discussion. The results of the queries showed better performance of different roll up and drill down queries compared to detailed data queries

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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 (July 1, 2013): 47–53. http://dx.doi.org/10.5815/ijisa.2013.08.06.

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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 (April 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) neural network (WRBF) and feature subset harmony search based fuzzy discernibility classifier (HSFD) approaches are proposed as a data mining technique for image segmentation based classification. In this paper, the authors use Lena RGB image; Magnetic resonance image (MR) and Computed Tomography (CT) Image for analysis. It is observed from the obtained simulation results that Wavelet based RBF neural network outperforms the harmony search based fuzzy discernibility classifiers.

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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 (April 1, 2022): 316–21. http://dx.doi.org/10.3844/jcssp.2022.316.321.

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Abedini, Mohammadali, Farzaneh Ahmadzadeh, and Rassoul Noorossana. "Customer credit scoring using a hybrid data mining approach." Kybernetes 45, no. 10 (November 7, 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. Finally, two-level majority voting schemes help the approach to achieve more accuracy. Findings A comparison of results shows the proposed model outperforms powerful single classifiers such as multilayer perceptron (MLP), support vector machine, logistic regression (LR). In addition, it is more accurate than ensemble learning methods such as bagging-LR or rotation forest (RF)-MLP. The model outperforms single classifiers in terms of type I and II errors; it is close to some ensemble approaches such as bagging-LR and RF-MLP but fails to outperform them in terms of type I and II errors. Moreover, majority voting in the final stage provides more reliable results. Practical implications The study concludes the approach would be beneficial for banks, credit card companies and other credit provider organisations. Originality/value A novel four stages hybrid approach inspired by bagging ensemble method proposed. Moreover the two-level majority voting in two different schemes in the last stage provides more accuracy. An integrated evaluation criterion for classification errors provides an enhanced insight for error comparisons.

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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 (July 8, 2017): 38–46. http://dx.doi.org/10.5815/ijieeb.2017.04.06.

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Jamalian, E., and R. Foukerdi. "A Hybrid Data Mining Method for Customer Churn Prediction." Engineering, Technology & Applied Science Research 8, no. 3 (June 19, 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 predicts customers churn more accurately, using data fusion and feature extraction techniques. After data preparation and feature selection, two algorithms, LOLIMOT and C5.0, were trained with different size of features and performed on test data. Then the outputs of the individual classifiers were combined with weighted voting. The results of applying this method on real data of a telecommunication company proved the effectiveness of the method.

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

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Mehla, Stuti, and Sanjeev Rana. "A Hybrid Approach for Opinion Mining Using Twitter Data." Journal of Computational and Theoretical Nanoscience 16, no. 9 (September 1, 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 opinions are extracted. Since posted messages are short in nature and it results sparse feature matrix giving less values of performance matrix. In this research paper we have proposed Optimised Feature Opinion Classifier for Big Data termed as OFOCBD which works on the principle of optimizing the classifiers giving improved values of performance matrix. In this research paper our proposed algorithms have shown better results as the data is converted into big data.

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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 (December 2007): 703–18. http://dx.doi.org/10.1016/j.knosys.2006.10.003.

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Vatsavai, Ranga Raju, and Budhendra Bhaduri. "A hybrid classification scheme for mining multisource geospatial data." GeoInformatica 15, no. 1 (July 22, 2010): 29–47. http://dx.doi.org/10.1007/s10707-010-0113-4.

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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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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 (May 28, 2023): 219–28. http://dx.doi.org/10.47392/irjash.2023.s029.

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Lang, Susan, and Craig Baehr. "Data Mining: A Hybrid Methodology for Complex and Dynamic Research." College Composition & Communication 64, no. 1 (September 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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Dr. T. Senthil Kumar. "Data Mining Based Marketing Decision Support System Using Hybrid Machine Learning Algorithm." September 2020 2, no. 3 (August 28, 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 are need to planned and executed. Marketing decision support system helps to reduce the organization burdens in analysing and strategical planning through its efficient data mining approach. This research work proposed a data mining based decision support system using decision tree and artificial neural network as a hybrid approach to estimate the marketing strategies for an organization.

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Dr. T. Senthil Kumar. "Data Mining Based Marketing Decision Support System Using Hybrid Machine Learning Algorithm." September 2020 2, no. 3 (August 28, 2020): 185–93. http://dx.doi.org/10.36548/jaicn.2020.3.007.

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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 are need to planned and executed. Marketing decision support system helps to reduce the organization burdens in analysing and strategical planning through its efficient data mining approach. This research work proposed a data mining based decision support system using decision tree and artificial neural network as a hybrid approach to estimate the marketing strategies for an organization.

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Tseng, Tzu-Liang (Bill), Fuhua Jiang, and Yongjin (James) Kwon. "Hybrid Type II fuzzy system & data mining approach for surface finish." Journal of Computational Design and Engineering 2, no. 3 (March 5, 2015): 137–47. http://dx.doi.org/10.1016/j.jcde.2015.02.002.

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Abstract In this study, a new methodology in predicting a system output has been investigated by applying a data mining technique and a hybrid type II fuzzy system in CNC turning operations. The purpose was to generate a supplemental control function under the dynamic machining environment, where unforeseeable changes may occur frequently. Two different types of membership functions were developed for the fuzzy logic systems and also by combining the two types, a hybrid system was generated. Genetic algorithm was used for fuzzy adaptation in the control system. Fuzzy rules are automatically modified in the process of genetic algorithm training. The computational results showed that the hybrid system with a genetic adaptation generated a far better accuracy. The hybrid fuzzy system with genetic algorithm training demonstrated more effective prediction capability and a strong potential for the implementation into existing control functions. Highlights A new methodology in predicting a CNC machining output has been investigated. A data mining technique and a hybrid type II fuzzy system are applied. Two different types of membership functions were created to generate a hybrid system. Fuzzy rules are automatically modified in the process of genetic algorithm training. The results showed that the hybrid system generated a far better accuracy.

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Doreswamy and K. S. Hemanth. "Hybrid Data Mining Technique for Knowledge Discovery from Engineering Materials Data Sets." International Journal of Database Management Systems 3, no. 1 (February 28, 2011): 166–77. http://dx.doi.org/10.5121/ijdms.2011.3111.

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Schaefer, G., and T. Nakashima. "Data Mining of Gene Expression Data by Fuzzy and Hybrid Fuzzy Methods." IEEE Transactions on Information Technology in Biomedicine 14, no. 1 (January 2010): 23–29. http://dx.doi.org/10.1109/titb.2009.2033590.

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Singh, Arjun, and Amit Saxena. "A Hybrid Data Model for Prediction of Disaster using Data Mining Approaches." International Journal of Engineering Trends and Technology 41, no. 7 (November 25, 2016): 384–92. http://dx.doi.org/10.14445/22315381/ijett-v41p270.

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Park, Hee-Chang, and Kwang-Hyun Cho. "Application Scheme of Hybrid Data Mining for Fused Data in Statistical Survey." Korean Journal of Applied Statistics 21, no. 3 (June 30, 2008): 399–411. http://dx.doi.org/10.5351/kjas.2008.21.3.399.

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