Search Results - (( java segmentation mining algorithm ) OR ( program value mining algorithm ))
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1
Image clustering comparison of two color segmentation techniques
Published 2010“…Finally, the algorithm found, which would solve the image segmentation problem.…”
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Thesis -
2
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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3
Prime-based method for interactive mining of frequent patterns
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Case Slicing Technique for Feature Selection
Published 2004“…Finding a good classification algorithm is an important component of many data mining projects. …”
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6
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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Propositional satisfiability method in rough classification modeling for data mining
Published 2002“…Two models, Standard Integer Programming (SIP) and Decision Related Integer Programming (DRIP) to represent the minimal reduct computation problem were proposed. …”
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Long-term electrical energy consumption: Formulating and forecasting via optimized gene expression programming / Seyed Hamidreza Aghay Kaboli
Published 2018“…In the developed feature selection approach, multi-objective binary-valued backtracking search algorithm (MOBBSA) is used as an efficient evolutionary search algorithm to search within different combinations of input variables and selects the non-dominated feature subsets, which minimize simultaneously both the estimation error and the number of features. …”
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