Search Results - (( software aggregation generation algorithm ) OR ( java application mining algorithm ))

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  1. 1
    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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  2. 2
    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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  3. 3
    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
    “…Attached sensors generate data and send these data to the Java Servlet application through a WIFI module. These data are processed and stored in appropriate formats in a MySQL server database. …”
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  4. 4
    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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  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
    Simulation on Concrete Fracture on Mesoscale Structure By Using Lattice Element

    Simulation on Concrete Fracture on Mesoscale Structure By Using Lattice Element by Rozilawati Kamarudin, Rozilawati

    Published 2009
    “…The simulation started from the executing the data that input by user in program language C++ and will be graphically determined in the AutoCAD software which shows the location of particles. In executed the data, the author will use the Bloodshed DevC++ software to gather the data and determine the location of aggregate by giving the coordinate of the centre of particle and lattice network generated. …”
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  7. 7
    Simulation on Concrete Fracture on Mesoscale structure by using Lattice element

    Simulation on Concrete Fracture on Mesoscale structure by using Lattice element by Rozilawati Kamaruddin, Rozilawati

    Published 2009
    “…The simulation started from the executing the data that input by user in program language C++ and will be graphically determined in the AutoCAD software which shows the location of particles. In executed the data, the author will use the Bloodshed DevC++ software to gather the data and determine the location of aggregate by giving the coordinate of the centre of particle and lattice network generated. …”
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  8. 8
    Simulation on Concrete Fracture on Mesoscale Structure By Using Lattice Element

    Simulation on Concrete Fracture on Mesoscale Structure By Using Lattice Element by Rozilawati Kamarudin, Rozilawati

    Published 2009
    “…The simulation started from the executing the data that input by user in program language C++ and will be graphically determined in the AutoCAD software which shows the location of particles. In executed the data, the author will use the Bloodshed DevC++ software to gather the data and determine the location of aggregate by giving the coordinate of the centre of particle and lattice network generated. …”
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  9. 9
    Simulation on Concrete Fracture on Mesoscale Structure By Using Lattice Element

    Simulation on Concrete Fracture on Mesoscale Structure By Using Lattice Element by Rozilawati Kamarudin, Rozilawati

    Published 2009
    “…The simulation started from the executing the data that input by user in program language C++ and will be graphically determined in the AutoCAD software which shows the location of particles. In executed the data, the author will use the Bloodshed DevC++ software to gather the data and determine the location of aggregate by giving the coordinate of the centre of particle and lattice network generated. …”
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  10. 10
    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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    Citation Index Journal
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  11. 11
    A novel rank aggregation-based hybrid multifilter wrapper feature selection method in software defect prediction

    A novel rank aggregation-based hybrid multifilter wrapper feature selection method in software defect prediction by Balogun, A.O., Basri, S., Mahamad, S., Capretz, L.F., Imam, A.A., Almomani, M.A., Adeyemo, V.E., Kumar, G.

    Published 2021
    “…The first stage involves a rank aggregation-based multifilter feature selection (RMFFS) method that addresses the filter rank selection problem by aggregating individual rank lists from multiple filter methods, using a novel rank aggregation method to generate a single, robust, and non-disjoint rank list. …”
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  12. 12
    Spatial Simulation for Flood Disaster Forecasting: A Geospatial Technology-based Group Analytic Hierarchy Process Approach

    Spatial Simulation for Flood Disaster Forecasting: A Geospatial Technology-based Group Analytic Hierarchy Process Approach by Dano Lawal, Umar, Matori, Abdul Nasir, Wan Yusof, Khamaruzaman, Mustafa Hashim , Ahmad, Aminu, Mansir, Sabri, Soheil, Balogun, Abdul-Lateef, Ahmed Chandio, Imtiaz, Radin Mohd Mokhtar, Munirah

    Published 2013
    “…In validating the robustness of the result, known Flood Extent Extraction Model developed based on a Neural Network Algorithm embedded in ENVI 4.8 software was used. …”
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  13. 13
    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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  14. 14
    CHID : conditional hybrid intrusion detection system for reducing false positives and resource consumption on malicous datasets

    CHID : conditional hybrid intrusion detection system for reducing false positives and resource consumption on malicous datasets by Alaidaros, Hashem Mohammed

    Published 2017
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