Search Results - (( developing function method algorithm ) OR ( data application mining algorithm ))
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1
Logistic regression methods for classification of imbalanced data sets
Published 2012“…Hence, it is required to develop effective imbalanced LR-based methods to be widely used in data mining applications. …”
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2
A hybrid approach for artificial immune recognition system / Mahmoud Reza Saybani
Published 2016“…The increasing size of data being stored have created the need for computer-based methods for automatic data analysis. Many researchers, who have developed methods and algorithms within the field of artificial intelligence, machine learning and data mining, have addressed extracting useful information from the data. …”
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3
The Parallel Fuzzy C-Median Clustering Algorithm Using Spark for the Big Data
Published 2024“…The comparison analysis exhibits that our suggested approach outperforms the others, especially for computational time. The developed approach is benchmarked with the existing methods such as MiniBatchKmeans, AffinityPropagation, SpectralClustering, Ward, OPTICS, and BRICH in terms of silhouette index and cost function.…”
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4
Intelligent transmission line fault diagnosis using the Apriori associated rule algorithm under cloud computing environment
Published 2024“…The leakage fault cases verify the algorithm�s applicability and complete the correlation diagnosis of water wall leakage fault. …”
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Web based clustering tool using K-MEAN++ algorithm / Muhammad Nur Syazwanie Aznan
Published 2019“…This project will use the rapid application development (RAD) methodology since this are the most suitable method for developing the system. …”
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6
Multiview Laplacian semisupervised feature selection by leveraging shared knowledge among multiple tasks
Published 2019“…We develop an efficient iterative algorithm to optimize it since the objective function of the proposed method is non-smooth and difficult to solve. …”
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7
Data Analysis and Rating Prediction on Google Play Store Using Data-Mining Techniques
Published 2022“…This biggest Android Application (App) provides a wide variety of details on requirements such as reviews, quality, number of installs, and explanations for device functionality. This study aims to predict the ratings of Google Play Store apps using decision trees for classification in machine learning algorithms. …”
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8
Analysis of Traffic Accident Patterns Using Association Rule Mining
Published 2024“…This study analyzed the levels of minor, moderate, and severe traffic accidents in the Palembang Police area from 2015 to 2020 using association rule mining and the apriori algorithm. The study established valuable insights into accident trends and contributing factors by leveraging traffic accident data and determining variable relationships. …”
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9
Application of Optimization Methods for Solving Clustering and Classification Problems
Published 2011“…This method does not explicitly use derivatives, and is particularly appropriate when functions are non-smooth. …”
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10
Density subspace clustering: a case study on perception of the required skill
Published 2014“…DAMIRA successfully clustered all of the data, while INSCY method has a lower coverage than FIRES method. For F1 Measure, SUBCLU method is better than FIRES, INSCY, and DAMIRA. …”
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11
Spatial Data Mining Model For Landfill Sites Suitability Mapping Based On Neural Networks And Multivariate Analysis
Published 2017“…There are several Spatial Data Mining (SDM) methods and Multi Criteria Decision Analysis (MCDA) workflows that are currently available, but their application in landfill sites selection is limited and reveals a number of drawbacks. …”
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12
Design and evaluation on adaptive fuzzy speed control of mobile robot
Published 2011“…The effectiveness of the control system is verified through real time experiments in an indoor environment with different slope. Both data acquisition and control algorithm are developed by using LabVIEW. …”
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13
Application of target detection method based on convolutional neural network in sustainable outdoor education
Published 2023“…Different recognition methods have certain advantages in accuracy, speed, and other aspects. …”
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14
Improvement on rooftop classification of worldview-3 imagery using object-based image analysis
Published 2019“…Then, the classifier (support vector machine (SVM) and data mining (DM) algorithm, decision tree (DT) were applied on each fusion image and their accuracy were evaluated. …”
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15
Improving Extreme Programming Framework With Security Concerns For E-Commerce Applications
Published 2024thesis::doctoral thesis -
16
Integrated framework with association analysis for gene selection in microarray data classification
Published 2011“…To achieve that, an integrated framework with a new gene selection method was developed to improve classification performance in terms of accuracy and number of selected genes. …”
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17
Clustering algorithm for market-basket analysis : the underlying concept of data mining technology
Published 2003“…The clustering algorithm based on Small Large Ratios, SLR is presented in a manner that helps to understand the concept of data mining technology in marketbasket analysis. …”
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18
Modifying iEclat algorithm for infrequent patterns mining
Published 2018“…Pattern ruining has been extensively studied in research due to its successful application in several data mining scenarios. Association rules mining is a basic step to determine the correlation between data items based on frequency of occurrence. …”
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19
Web Usage Mining for UUM Learning Care Using Association Rules
Published 2004“…The enormous of information on the World Wide Web makes it obvious candidate for data mining research. Application of data mining techniques to the World Wide Web referred as Web mining where this term has been used in three distint ways; Web Content Mining, Web Structure Mining and Web Usage Mining. …”
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20
Modifying iEclat algo ithm for infrequent patterns mining
Published 2018“…Pattern ruining has been extensively studied in research due to its successful application in several data mining scenarios. Association rules mining is a basic step to determine the correlation between data items based on frequency of occurrence. …”
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