Search Results - (( program mining ((data algorithm) OR (tree algorithm)) ) OR ( java implication based algorithm ))
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Evaluation of data mining classification and clustering techniques for diabetes / Tuba Pala and Ali Yilmaz Camurcu
Published 2014“…The success evaluation of data mining classification algorithms have been realized through the data mining programs Weka and RapidMiner. …”
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Case Slicing Technique for Feature Selection
Published 2004“…One of the problems addressed by machine learning is data classification. Finding a good classification algorithm is an important component of many data mining projects. …”
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Thesis -
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Prime-based method for interactive mining of frequent patterns
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Tracking student performance in introductory programming by means of machine learning
Published 2023“…Big data; Decision trees; Education computing; Learning algorithms; Learning systems; Machine learning; Smart city; Students; Trees (mathematics); Educational data mining; Educational institutions; Hidden patterns; Introductory programming; Introductory programming course; Student performance; Student's performance; Weka; Data mining…”
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Decision Tree: Customer churn analysis for a loyalty program using data mining algorithm
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Classification model for hotspot occurrences using spatial decision tree algorithm
Published 2013“…This study describes the application of data mining technique namely decision tree on forest fires data. …”
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Building customer churn prediction models in Indonesian telecommunication company using decision tree algorithm
Published 2023“…Preventive efforts are needed by predicting the value of churn in the future. This study uses data mining techniques with decision tree algorithms to predict customer churn in one of Indonesian Telecommunication companies. …”
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Predicting Student Performance in Object Oriented Programming Using Decision Tree : A Case at Kolej Poly-Tech Mara, Kuantan
Published 2013“…Using 10-fold cross validation for each algorithm, it was found that decision tree was the best algorithm with 83.6944% correctness. …”
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Twofold Integer Programming Model for Improving Rough Set Classification Accuracy in Data Mining.
Published 2005“…The accuracy for rules and classification resulted from the TIP method are compared with other methods such as Standard Integer Programming (SIP) and Decision Related Integer Programming (DRIP) from Rough Set, Genetic Algorithm (GA), Johnson reducer, HoltelR method, Multiple Regression (MR), Neural Network (NN), Induction of Decision Tree Algorithm (ID3) and Base Learning Algorithm (C4.5); all other classifiers that are mostly used in the classification tasks. …”
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An efficient and effective case classification method based on slicing
Published 2006“…The paper also discusses two of common classification algorithms that are used either in data mining or in general AI. …”
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Forecast of Muslimah fashion trends in Caca's company / Muhammad Saifullah Mohd Taip
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Student Project -
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Exploring frogeye leaf spot disease severity in soybean through hyperspectral data analysis and machine learning with Orange Data Mining
Published 2025“…No previous study has investigated Orange mining tool as visual programming approach in analysing hyperspectral reflectance data, especially in crop disease detection. …”
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Towards a better feature subset selection approach
Published 2010“…The selection of the optimal features subset and the classification has become an important issue in the data mining field.We propose a feature selection scheme based on slicing technique which was originally proposed for programming languages.The proposed approach called Case Slicing Technique (CST).Slicing means that we are interested in automatically obtaining that portion 'features' of the case responsible for specific parts of the solution of the case at hand.We show that our goal should be to eliminate the number of features by removing irrelevant once.Choosing a subset of the features may increase accuracy and reduce complexity of the acquired knowledge.Our experimental results indicate that the performance of CST as a method of feature subset selection is better than the performance of the other approaches which are RELIEF with Base Learning Algorithm (C4.5), RELIEF with K-Nearest Neighbour (K-NN), RELIEF with Induction of Decision Tree Algorithm (ID3) and RELIEF with Naïve Bayes (NB), which are mostly used in the feature selection task.…”
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Classification models for higher learning scholarship award decisions
Published 2018“…Five algorithms were employed to develop a classification model in determining the award of the scholarship, namely J48, SVM, NB, ANN and RT algorithms. …”
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Propositional satisfiability method in rough classification modeling for data mining
Published 2002“…The goal of data mining is to find rules that model the world sufficiently well. …”
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Data mining using genetic algorithm in finance data / A. Noor Latiffah and A. B. Nordin
Published 2006“…Data mining can discover patterns or rules from a vast volume of data. …”
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Classification of students' performance in computer programming course according to learning style
Published 2024Subjects:Conference Paper -
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Genetic Algorithm for Web Data Mining
Published 2001“…This study used a prototype program based on genetic algorithm to manipulate the initial set of data. …”
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