Search Results - (( java implication based algorithm ) OR ( knowledge reduction mining algorithm ))
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Document classification based on kNN algorithm by term vector space reduction
Published 2023Conference Paper -
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Twofold Integer Programming Model for Improving Rough Set Classification Accuracy in Data Mining.
Published 2005“…These tools are known as Data Mining (DM). One aims of DM is to discover decision rules for extracting meaningful knowledge. …”
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Data mining reduction methods and performances of rules
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Towards lowering computational power in IoT systems: Clustering algorithm for high-dimensional data stream using entropy window reduction
Published 2024“…This paper introduces an Entropy Window Reduction (EWR) algorithm, which is an improved version of the BOCEDS technique. …”
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Feature and Instances Selection for Nearest Neighbor Classification via Cooperative PSO
Published 2014“…Data reduction is an essential task in the data preparation phase of knowledge discovery and data mining (KDD). …”
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Discovering decision algorithm from a distance relay event report
Published 2009“…In this study rough-set-based data mining strategy was formulated to discover distance relay decision algorithm from its resident event report. …”
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Propositional satisfiability method in rough classification modeling for data mining
Published 2002“…In rough set approach to data mining, the set of interesting rules are determined using a notion of reduct. …”
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8
Evaluation and optimization of frequent, closed and maximal association rule based classification
Published 2014“…Real world applications of association rule mining have well-known problems of discovering a large number of rules, many of which are not interesting or useful for the application at hand.The algorithms for closed and maximal item sets mining significantly reduce the volume of rules discovered and complexity associated with the task, but the implications of their use and important differences with respect to the generalization power, precision and recall when used in the classification problem have not been examined.In this paper, we present a systematic evaluation of the association rules discovered from frequent, closed and maximal item set mining algorithms, combining common data mining and statistical interestingness measures, and outline an appropriate sequence of usage.The experiments are performed using a number of real-world datasets that represent diverse characteristics of data/items, and detailed evaluation of rule sets is provided as a whole and w.r.t individual classes. …”
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A new model for iris data set classification based on linear support vector machine parameter's optimization
Published 2020“…The SVM is a one technique of machine learning techniques that is well known technique, learning with supervised and have been applied perfectly to a vary problems of: regression, classification, and clustering in diverse domains such as gene expression, web text mining. In this study, we proposed a newly mode for classifying iris data set using SVM classifier and genetic algorithm to optimize c and gamma parameters of linear SVM, in addition principle components analysis (PCA) algorithm was use for features reduction.…”
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Problem restructuring in interger programming for reduct searching
Published 2003“…In effect, they are very useful in generating rules when solving the classification problem that is inherent in data mining. The thesis emphasizes mainly on the improvement of the original SIP/DRIP algorithm in term of performance. …”
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Digital Quran With Storage Optimization Through Duplication Handling And Compressed Sparse Matrix Method
Published 2024thesis::doctoral thesis -
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Machine-learning-based adaptive distance protection relay to eliminate zone-3 protection under-reach problem on statcom-compensated transmission lines
Published 2020“…The hybrid discrete wavelet multiresolution analyses and machine learning (DWMRA-ML) algorithm is deployed to discover the hidden useful knowledge extraction from the 1-cycle short circuit transient fault signals (voltage and current) from healthy and fault lines section. …”
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