2/2/2024 0 Comments Spacenet classifier decisionWitten IH, Frank E, Hall MA (2011) Data mining: practical machine learning tools and techniques, 3rd edn. Quinlan JR (1986) Induction of decision tree. Quinlan J (1993) C4.5: programs for machine learning. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M (2011) Édouard Duchesnay: Scikit-learn: Machine learning in python. Özköse H, Arı ES, Gencer C (2015) Yesterday, today and tomorrow of big data. L’heureux A, Grolinger K, Elyamany HF, Capretz MAM (2017) Machine learning with big data: challenges and approaches. Jain AK (2010) Data clustering: 50 years beyond k-means. Han J, Kamber M, Pei J (2011) Data mining concepts and techniques, 3rd edn. Exp Syst Appl 40(15):5895–5906įarid DM, Zhang L, Rahman CM, Hossain M, Strachan R (2014) Hybrid decision tree and naïve bayes classifiers for multi-class classification tasks. J Adv Inf Technol 4(3):129–135įarid DM, Zhang L, Hossain A, Rahman CM, Strachan R, Sexton G, Dahal K (2013) An adaptive ensemble classifier for mining concept drifting data streams. Int J Data Min Knowl Manag Process 3(1):129–135įarid DM, Rahman CM (2013) Mining complex data streams: discretization, attribute selection and classification. In: Future technologies conference, San Francisco, United States, pp 260–268įarid DM, Rahman CM (2013) Assigning weights to training instances increases classification accuracy. Exp Syst Appl 64:305–316įarid DM, Nowé A, Manderick B (2016) A feature grouping method for ensemble clustering of high-dimensional genomic big data. ACM SIGKDD Explor Newsl 14(2):1–5įarid DM, Al-Mamun MA, Manderick B, Nowe A (2016) An adaptive rule-based classifier for mining big biological data. įan W, Bifet A (2013) Mining big data: current status, and forecast to the future. Pattern Recognit 45(1):434–446ĭheeru D, Taniskidou EK (2017) UCI machine learning repository. Chapman and Hall/CRC, Boca RatonĬhen X, Ye Y, Xu X, Huang JZ (2012) A feature group weighting method for subspace clustering of high-dimensional data. Chapman and Hall/CRC, Boca Ratonīreiman L, Friedman J, Stone CJ, Olshen RA (1984) Classification and regression trees. Chapman and Hall/CRC data mining and knowledge discovery series. KeywordsĪggarwal CC, Reddy CK (eds) (2013) Data clustering: algorithms and applications. We have collected 10 big data from the UC Irvine machine learning repository for experimental analysis and applied three popular decision tree induction algorithms: ID3 (Iterative Dichotomiser 3), C4.5 (extension of ID3 algorithm), and CART (Classification and Regression Tree) for classifier construction. ![]() However, we can relate to the clusters with class labels. The proposed approach clusters the big data and builds the classifier based on the clusters without considering the class labels, which basically improve the performance of the classifier. Data labelling is always very costly and time-consuming process, and it becomes a very difficult task if the data is big data. In this paper, we have investigated if we can build a classification model based on the similarities of the instances instead of class labels of instances. In machine learning for data mining applications, the classification models are trained based on labelled training datasets. The objective of a classification model is to correctly predict the categorical class labels of known/unknown instances. ![]() Data classification in supervised learning is the process of classifying data for data mining task that helps to analyse data for decision-making.
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