CLASSIFICATION & PREDICTION TECHNIQUES IN DATA MINING: A REVIEW

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Roshan Jahan
Nida Khan
Preetam Suman
Deepak Kumar Singh

Abstract

Classification and prediction are two important terms in data warehouse. The term classification denotes the class of object, and the term prediction is use to predict the result based on analysis. These both terms are equally responsible for data analysis. There are various issues which effects classification and prediction. This paper summarizes the issues and various techniques related to classification and prediction. There are four important techniques discussed in paper, they are Decision Tree, Bayesian Classification, Back Propagation and Nearest Neighbor Classification. Paper also discussed current research done by researchers.

 

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References

Jiawei Han, Jian Pei, Micheline Kamber “The Morgan Kaufmann Series in Data Management Systems†Elsevier, 2011.

Ian H. Witten, Eibe Frank, Mark A. Hall, Christopher J. Pal, “The Morgan Kaufmann Series in Data Management Systems†Morgan Kaufmann, 2016.

Z. Wang, X. Huang, Y. Song and J. Xiao, "An outlier detection algorithm based on the degree of sharpness and its applications on traffic big data preprocessing," 2017 IEEE 2nd International Conference on Big Data Analysis (ICBDA)(, Beijing, 2017, pp. 478-482.

J. Hurtado-Rincon, S. Rojas-Jaramillo, Y. Ricardo-Cespedes, A. M. Ãlvarez-Meza and G. Castellanos-Domínguez, "Motor imagery classification using feature relevance analysis: An Emotiv-based BCI system," 2014 XIX Symposium on Image, Signal Processing and Artificial Vision, Armenia, 2014, pp. 1-5.

L. Ordonez-Ante, T. Vanhove, G. Van Seghbroeck, T. Wauters, B. Volckaert and F. De Turck, "Dynamic data transformation for low latency querying in big data systems," 2017 IEEE International Conference on Big Data (Big Data), Boston, MA, USA, 2017, pp. 2480-2489.

A. Fanelli, D. Micucci, M. Mobilio and F. Tisato, "Spatio-temporal normalization of data from heterogeneous sensors," 2015 10th International Joint Conference on Software Technologies (ICSOFT), Colmar, 2015, pp. 1-6.

K. V. Isabella, L. Sampebatu and I. Albarda, "Analysis of earthquake magnitude level based on data Twitter with decision tree algorithm," 2017 International Conference on Information Technology Systems and Innovation (ICITSI), Bandung, Indonesia, 2017, pp. 73-76.

D. M. Safitri and I. Surjandari, "Travel mode switching prediction using decision tree in Jakarta greater area," 2017 International Conference on Information Technology Systems and Innovation (ICITSI), Bandung, Indonesia, 2017, pp. 246-250.

R. Larasati and H. KeungLam, "Handwritten digits recognition using ensemble neural networks and ensemble decision tree," 2017 International Conference on Smart Cities, Automation & Intelligent Computing Systems (ICON-SONICS), Yogyakarta, Indonesia, 2017, pp. 99-104.

P. Su, J. Yang, Z. Li and Y. Liu, "Mining Actionable Behavioral Rules Based on Decision Tree Classifier," 2017 13th International Conference on Semantics, Knowledge and Grids (SKG), Beijing, China, 2017, pp. 139-143.

F. Chiheb, F. Boumahdi, H. Bouarfa and D. Boukraa, "Predicting students performance using decision trees: Case of an Algerian University," 2017 International Conference on Mathematics and Information Technology (ICMIT), Adrar, Algeria, 2017, pp. 113-121.

A. Desai and S. Chaudhary, "Distributed decision tree v.2.0," 2017 IEEE International Conference on Big Data (Big Data), Boston, MA, USA, 2017, pp. 929-934.

S. Boluki, M. Shahrokh Esfahani, X. Qian and E. R. Dougherty, "Constructing Pathway-based Priors Within a Gaussian Mixture Model for Bayesian Regression and Classification," in IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. PP, no. 99, pp. 1-1.

M. Li, K. M. de Beurs, A. Stein and W. Bijker, "Incorporating Open Source Data for Bayesian Classification of Urban Land Use From VHR Stereo Images," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 10, no. 11, pp. 4930-4943, Nov. 2017.

D. Wang, Y. Song and C. Zhao, "Bayesian classification based service-awareness in software defined optical network for big data services," 2017 IEEE 2nd International Conference on Big Data Analysis (ICBDA)(, Beijing, 2017, pp. 589-592.

K. Lei, L. Zhang, Y. Shen, X. Huang and J. Wu, "Syndromes diagnostic model for coronary artery disease (CAD): An improved naïve Bayesian classification model based on attribute relevancy," 2017 IEEE 2nd International Conference on Big Data Analysis (ICBDA)(, Beijing, 2017, pp. 897-902.

M. Ali and M. Antolovich, "Classification on Grassmann Manifold via Scheiddegger-Watson Distribution using Bayesian Approach," 2016 3rd International Conference on Soft Computing & Machine Intelligence (ISCMI), Dubai, 2016, pp. 28-32.

D. Fan et al., "Effectively Measuring Respiratory Flow with Portable Pressure Data using Back Propagation Neural Network," in IEEE Journal of Translational Engineering in Health and Medicine, vol. PP, no. 99, pp. 1-1.

J. Kajornrit and P. Chaipornkaew, "A comparative study of ensemble back-propagation neural network for the regression problems," 2017 2nd International Conference on Information Technology (INCIT), Nakhonpathom, 2017, pp. 1-6.

Y. C. Tsai and K. W. Lin, "Application of back propagation neuron network on data linkage transmission of semiconductor hydrogen detection device," 2017 IEEE 8th International Conference on Awareness Science and Technology (iCAST), Taichung, Taiwan, 2017, pp. 268-272.

G. M. Fuady et al., "Extreme learning machine and back propagation neural network comparison for temperature and humidity control of oyster mushroom based on microcontroller," 2017 International Symposium on Electronics and Smart Devices (ISESD), Yogyakarta, Indonesia, 2017, pp. 46-50.

P. Rosero-Montalvo et al., "Prototype reduction algorithms comparison in nearest neighbor classification for sensor data: Empirical study," 2017 IEEE Second Ecuador Technical Chapters Meeting (ETCM), Salinas, 2017, pp. 1-5.

Y. K. Noh, B. T. Zhang and D. D. Lee, "Generative Local Metric Learning for Nearest Neighbor Classification," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 40, no. 1, pp. 106-118, Jan. 1 2018.

S. Ezghari, R. Benouini, A. Zahi and K. Zenkouar, "Learning efficient and interpretable prototypes from data for nearest neighbor classification method," 2017 Intelligent Systems and Computer Vision (ISCV), Fez, 2017, pp. 1-7.

S. Ramírez-Gallego, B. Krawczyk, S. García, M. Woźniak, J. M. Benítez and F. Herrera, "Nearest Neighbor Classification for High-Speed Big Data Streams Using Spark," in IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 47, no. 10, pp. 2727-2739, Oct. 2017.

H. Ma, J. Gou, X. Wang, J. Ke and S. Zeng, "Sparse Coefficient-Based ${k}$ -Nearest Neighbor Classification," in IEEE Access, vol. 5, pp. 16618-16634, 2017.

J. Maillo, J. Luengo, S. García, F. Herrera and I. Triguero, "Exact fuzzy k-nearest neighbor classification for big datasets," 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Naples, 2017, pp. 1-6.