Search Results - (( java implementation mining algorithm ) OR ( parameters problem tree algorithm ))
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Direct approach for mining association rules from structured XML data
Published 2012“…The thesis also provides a two different implementation of the modified FLEX algorithm using a java based parsers and XQuery implementation. …”
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Thesis -
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Study and Implementation of Data Mining in Urban Gardening
Published 2019“…Using the J48 tree algorithm implemented through WEKA API on a Java Servlet, data provided is processed to derive a health index of the plant, with the possible outcomes set to “Good,” “Okay”, or “Bad”. …”
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Scalable approach for mining association rules from structured XML data
Published 2009“…Many techniques have been proposed to tackle the problem of mining XML data we study the various techniques to mine XML data and yet We presented a java based implementation of FLEX algorithm for mining XML data.…”
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Mining association rules from structured XML data
Published 2009“…Many techniques have been proposed to tackle the problem of mining XML data. We study the various techniques to mine XML data and yet We presented a java based implementation of FLEX algorithm for mining XML data.…”
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A web-based implementation of k-means algorithms
Published 2022“…This stinginess of proximity measures in data mining tools is stifling the performance of the algorithm. …”
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Final Year Project / Dissertation / Thesis -
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Hyper-Heuristic Evolutionary Approach for Constructing Decision Tree Classifiers
Published 2021“…Classification and Regression Tree (CART) algorithm is one of the renowned decision tree induction algorithms to address the classification as well as regression problems. …”
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Tree-based contrast subspace mining method
Published 2020“…Hence, this thesis presents the optimization of parameters values for the tree-based method by genetic algorithm. …”
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Classification of breast cancer disease using bagging fuzzy-id3 algorithm based on fuzzydbd
Published 2022“…The bagging method was then applied to the FID3-DBD algorithm to overcome overfitting problems and high variance in decision trees. …”
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Image clustering comparison of two color segmentation techniques
Published 2010“…The clustering research is regarding the area of data mining and implementation of the clustering algorithms. …”
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Building customer churn prediction models in Indonesian telecommunication company using decision tree algorithm
Published 2023“…The best decision tree model has parameters of criterion information gain with a minimal gain = 0.01 and a max depth = 6. …”
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Parameter estimation and outlier detection in linear functional relationship model / Adilah Abdul Ghapor
Published 2017“…In the final part of the study on the missing value problem in LFRM, the modern imputation techniques, namely the expectation-maximization (EM) algorithm and the expectation-maximization with bootstrapping (EMB) algorithm is proposed. …”
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Features selection for intrusion detection system using hybridize PSO-SVM
Published 2016“…Hybridize Particle Swarm Optimization (PSO) as a searching algorithm and support vector machine (SVM) as a classifier had been implemented to cope with this problem. …”
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Wireless sensor nodes deployment using multi-robot based on improved spanning tree algorithm
Published 2015“…Developing an exploration algorithm based on spanning tree is the main contribution. …”
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Parameter estimation and outlier detection for some types of circular model / Siti Zanariah binti Satari
Published 2015“…Lastly, we consider the problem of detecting multiple outliers in circular regression models based on the clustering algorithm. …”
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Hybrid tabu search – strawberry algorithm for multidimensional knapsack problem
Published 2022“…In 2011, a new metaheuristic known as Strawberry algorithm (SBA) was initiated. Since then, it has been vastly applied to solve engineering problems. …”
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Classification of Google Play application using decision tree algorithm on sentiment analysis of text reviews / Aqil Khairy Hamsani, Ummu Fatihah Mohd Bahrin and Wan Dorishah Wan A...
Published 2023“…To achieve these objectives, the methods employed involve data preprocessing and implementing the Decision Tree (DT) algorithm for classification. The classification model is trained and tested using various split ratios, and the optimal depth for the DT is determined through parameter tuning to achieve the best accuracy. …”
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Intrusion Detection Systems, Issues, Challenges, and Needs
Published 2021“…However, these classifiers is not working well if they applied alone without any other algorithms that can tune the parameters of these classifiers or choose the best sub set features of the problem. …”
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