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  1. 1
    Study and Implementation of Data Mining in Urban Gardening

    Study and Implementation of Data Mining in Urban Gardening by Mohana, Muniandy, Lee, Eu Vern

    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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  2. 2
    Mining Sequential Patterns Using I-PrefixSpan

    Mining Sequential Patterns Using I-PrefixSpan by Dhany , Saputra, Rambli Dayang, R.A., Foong, Oi Mean

    Published 2007
    “…Sequential pattern mining is a relatively new data-mining problem with many areas of application. …”
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  3. 3
    Direct approach for mining association rules from structured XML data

    Direct approach for mining association rules from structured XML data by Abazeed, Ashraf Riad

    Published 2012
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  4. 4
    Separator Database and SPM Tree Framework for Mining Sequential Patterns Using Prefixspan with Pseudoprojection

    Separator Database and SPM Tree Framework for Mining Sequential Patterns Using Prefixspan with Pseudoprojection by Saputra , Dhany, Rambli, Dayang R.A., Foong, Oi Mean

    Published 2008
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  5. 5
    A web-based implementation of k-means algorithms

    A web-based implementation of k-means algorithms by Lee, Quan

    Published 2022
    “…This stinginess of proximity measures in data mining tools is stifling the performance of the algorithm. …”
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  6. 6
    Mining Sequential Patterns using I-PrefixSpan

    Mining Sequential Patterns using I-PrefixSpan by Saputra , Dhany, Dayang R.A. Rambli, Foong, Oi Mean

    Published 2008
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  7. 7
    Diagnosis and recommender system for diabetes patient using decision tree / Nurul Aida Mohd Zamary

    Diagnosis and recommender system for diabetes patient using decision tree / Nurul Aida Mohd Zamary by Mohd Zamary, Nurul Aida

    Published 2024
    “…This project aims to develop a decision-making support model for diabetes diagnosis and treatment recommendation using the decision tree algorithm. …”
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  8. 8
    Development of a Prediction Algorithm using Boosted Decision Trees for Earlier Diagnoses on Obstructive Sleep Apnea

    Development of a Prediction Algorithm using Boosted Decision Trees for Earlier Diagnoses on Obstructive Sleep Apnea by Sim, Doreen Ying Ying

    Published 2018
    “…This research develops a knowledge-based system by using computational intelligent approaches based on Boosting algorithms on decision trees augmented by pruning techniques and Association Rule Mining. …”
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  9. 9
    Semi-automatic oil palm tree counting from pleiades satellite imagery and airborne LiDAR / Nurul Syafiqah Khalid

    Semi-automatic oil palm tree counting from pleiades satellite imagery and airborne LiDAR / Nurul Syafiqah Khalid by Khalid, Nurul Syafiqah

    Published 2020
    “…This study aimed to develop the automatic oil palm tree counting using remote sensed data and two different algorithms at Felda Pasoh. …”
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  10. 10
    Improved Boosted Decision Tree Algorithms by Adaptive Apriori and Post-Pruning for Predicting Obstructive Sleep Apnea

    Improved Boosted Decision Tree Algorithms by Adaptive Apriori and Post-Pruning for Predicting Obstructive Sleep Apnea by Sim, Doreen Ying Ying, Teh, Chee Siong, Ahmad Izuanuddin, Ismail

    Published 2018
    “…The improved version of Boosted Decision Tree algorithm, named as Boosted Adaptive Apriori post-Pruned Decision Tree (Boosted AApoP-DT), was developed by referring to Adaptive Apriori (AA) properties and by using post-pruning technique. …”
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  11. 11
    Determination of tree height based on tree crown using algorithm derived from UAV imagery / Suzanah Abdullah ... [et al.]

    Determination of tree height based on tree crown using algorithm derived from UAV imagery / Suzanah Abdullah ... [et al.] by Abdullah, Suzanah, Tahar, Khairul Nizam, Abdul Rashid, Mohd Fadzil, Osoman, Muhammad Ariffin

    Published 2021
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  12. 12
    Improved Boosting Algorithms by Pre-Pruning and Associative Rule Mining on Decision Trees for predicting Obstructive Sleep Apnea

    Improved Boosting Algorithms by Pre-Pruning and Associative Rule Mining on Decision Trees for predicting Obstructive Sleep Apnea by Doreen Ying Ying, Sim, Chee Siong, Teh, Ahmad Izuanuddin, Ismail

    Published 2017
    “…An improved Boosting algorithm, named as Boosted PARM-DT, was developed by pre-pruning techniques and Associative Rule Mining (ARM) on decision trees built from the clinical datasets** collected for Obstructive Sleep Apnea (OSA). …”
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  13. 13
    Detecting Remote-To-Local (R2L) attack using Decision Tree algorithm / Ahmad Nasreen Aqmal Mohd Nordin

    Detecting Remote-To-Local (R2L) attack using Decision Tree algorithm / Ahmad Nasreen Aqmal Mohd Nordin by Mohd Nordin, Ahmad Nasreen Aqmal

    Published 2024
    “…The project successfully achieves its predetermined objectives, culminating in the development of an effective Remote to Local (R2L) Intrusion Detection System utilizing the Decision Tree algorithm. …”
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  14. 14
    Fast clock tree generation using exact zero skew clock routing algorithm

    Fast clock tree generation using exact zero skew clock routing algorithm by Reaz, Mamun Ibn, Ibrahimy, Muhammad Ibn, Amin, Nowshad

    Published 2009
    “…A Zero Skew clock routing methodology has been developed to help design team speed up their clock tree generation process. …”
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  15. 15
    Laptop price prediction using decision tree algorithm / Nurnazifah Abd Mokti

    Laptop price prediction using decision tree algorithm / Nurnazifah Abd Mokti by Abd Mokti, Nurnazifah

    Published 2024
    “…This research project focuses on developing a laptop price prediction model using the decision tree algorithm based on laptop specifications. …”
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  16. 16
    Features selection for intrusion detection system using hybridize PSO-SVM

    Features selection for intrusion detection system using hybridize PSO-SVM by Tabaan, Alaa Abdulrahman

    Published 2016
    “…The simulation will be carried on WEKA tool, which allows us to call some data mining methods under JAVA environment. The proposed model will be tested and evaluated on both NSL-KDD and KDD-CUP 99 using several performance metrics.…”
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  17. 17
    Classification model for hotspot occurrences using spatial decision tree algorithm

    Classification model for hotspot occurrences using spatial decision tree algorithm by Sitanggang, Imas Sukaesih, Yaakob, Razali, Mustapha, Norwati, Nuruddin, Ahmad Ainuddin

    Published 2013
    “…This study describes the application of data mining technique namely decision tree on forest fires data. We improved the ID3 decision tree algorithm such that it can be utilized on spatial data in order to develop a classification model for hotspots occurrence. …”
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  18. 18
    A class skew-insensitive ACO-based decision tree algorithm for imbalanced data sets

    A class skew-insensitive ACO-based decision tree algorithm for imbalanced data sets by Mohd Razali, Muhamad Hasbullah, Saian, Rizauddin, Yap, Bee Wah, Ku-Mahamud, Ku Ruhana

    Published 2021
    “…Ant-tree-miner (ATM) has an advantage over the conventional decision tree algorithm in terms of feature selection. …”
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  19. 19
    A decision tree based on spatial relationships for predicting hotspots in peatlands

    A decision tree based on spatial relationships for predicting hotspots in peatlands by Sitanggang, Imas Sukaesih, Yaakob, Razali, Mustapha, Norwati, Nuruddin, Ahmad Ainuddin

    Published 2014
    “…This work applied a spatial decision tree algorithm on spatial data of forest fires. The algorithm is the improvement of the conventional decision tree algorithm in which the distance and topological relationships are included to grow up spatial decision trees. …”
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  20. 20
    Extended spatial decision tree algorithm for classifying hotspot occurrence

    Extended spatial decision tree algorithm for classifying hotspot occurrence by Sitanggang, Imas Sukaesih

    Published 2013
    “…This work proposes a new spatial decision tree algorithm namely the extended spatial ID3 decision tree algorithm to classify hotspots occurrence from a forest fires dataset that contains point, line and polygon features. …”
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