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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
    Metaheuristic multi-hop clustering optimization for energy-efficient wireless sensor network

    Metaheuristic multi-hop clustering optimization for energy-efficient wireless sensor network by Vincent Chung, Norah Tuah, Kit Guan Lim, Min Keng Tan, Ismail Saad, Kenneth Tze Kin Teo

    Published 2020
    “…The cluster optimization algorithm will form a cluster network by dividing the sensor nodes into few clusters where each cluster has a cluster head (CH) for data collection. …”
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  3. 3
    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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  4. 4
    Clustering Methods For Cluster-Based Routing Protocols In Wireless Sensor Networks: Comparative Study

    Clustering Methods For Cluster-Based Routing Protocols In Wireless Sensor Networks: Comparative Study by Hassan, Ali Abdul Hussian, Md Shah, Wahidah, Jabbar Mohammed, Ali Abdul, Othman, Mohd Fairuz Iskandar

    Published 2017
    “…These approaches of clustering algorithms whether Distributed, Centralized, or Hybrid are reviewed very well, since the most of clustering algorithms have been developed by many researches based on these approaches. …”
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  5. 5
    Optimizing wireless sensor networks: A survey of clustering strategies and algorithms

    Optimizing wireless sensor networks: A survey of clustering strategies and algorithms by Hamim, Sakib Iqram, Azamuddin, Ab Rahman

    Published 2024
    “…This study highlights clustering techniques and methods that have been developed for WSNs, together with their objectives, concepts, and consequences on the performance of networks. …”
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  6. 6
    Cluster optimization in VANET using MFO algorithm and K-Means clustering

    Cluster optimization in VANET using MFO algorithm and K-Means clustering by Ramlee, Sham Rizal, Hasan, Sazlinah, K. Subramaniam, Shamala

    Published 2023
    “…Overall, the MFO Algorithm and K-Means algorithm can be used in combination to optimize the clustering in VANET, leading to better network performance, more reliable communication, and improved efficiency.…”
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  7. 7
    Developing multi-tier network design for effective energy consumption of cluster head selection in WSN / Wan Isni Sofiah Wan Din … [et al.]

    Developing multi-tier network design for effective energy consumption of cluster head selection in WSN / Wan Isni Sofiah Wan Din … [et al.] by Wan Din, Wan Isni Sofiah, Yahya, Saadiah, Taib, Mohd Nasir, Mohd Yassin, Ahmad Ihsan, Razali, Razulaimi

    Published 2016
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  8. 8
    Data mining in network traffic using fuzzy clustering

    Data mining in network traffic using fuzzy clustering by Mohamad, Shamsul

    Published 2003
    “…The fuzzy clustering process are made using three algorithms : Fuzzy C-Means (FCM), Gustafsof-Kessel (GK) and Gath-Geva (GG) algorithm. …”
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  9. 9
    Data mining in network traffic using fuzzy clustering

    Data mining in network traffic using fuzzy clustering by Mohamad, Shamsul

    Published 2003
    “…The fuzzy clustering process are made using three algorithms : Fuzzy C-Means (FCM), Gustafsof-Kessel (GK) and Gath-Geva (GG) algorithm. …”
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  10. 10
    Survey on Clustered Routing Protocols Adaptivity for Fire Incidents: Architecture Challenges, Data Losing, and Recommended Solutions

    Survey on Clustered Routing Protocols Adaptivity for Fire Incidents: Architecture Challenges, Data Losing, and Recommended Solutions by Abbas O.K., Abdullah F., Radzi N.A.M., Salman A.D., Abdulkadir S.J.

    Published 2025
    “…Many clustered routing algorithms have been developed to address various issues like energy efficiency, network lifetime, and hotspot problems. …”
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  11. 11
    Clustering Approach In Wireless Sensor Networks Based On K-Means: Limitations And Recommendations

    Clustering Approach In Wireless Sensor Networks Based On K-Means: Limitations And Recommendations by Hassan, Ali Abdul-hussian, Md Shah, Wahidah, Husien, Ali Mohamed, Talib, Mohammed Saad, Mohammed, Ali Abdul Jabbar, Othman, Mohd Fairuz Iskandar

    Published 2019
    “…Clustering approach in wireless sensor network is very important, the structure of cluster and how to improve it is a first challenge that faced the developers, because of it represent as a base for design the cluster-based routing protocol. …”
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  12. 12
    Multi-Hop Selective Constructive Interference Flooding Protocol For Wireless Sensor Networks

    Multi-Hop Selective Constructive Interference Flooding Protocol For Wireless Sensor Networks by Alhalabi, Huda A. H.

    Published 2019
    “…MSCIF integrates three main algorithms: clustering algorithm, selection algorithm, and a synchronized flooding. …”
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  13. 13
    Maximizing Lifetime of Homogeneous Wireless Sensor Network through Energy Efficient Clustering Method

    Maximizing Lifetime of Homogeneous Wireless Sensor Network through Energy Efficient Clustering Method by Hasan, Nurul

    Published 2010
    “…Energy efficiency is a very important issue for sensor nodes which affects the lifetime of sensor networks. To achieve energy balancing and maximizing network lifetime we divided the whole network into different clusters. …”
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  14. 14
    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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  15. 15
    Fuzzy clustering method and evaluation based on multi criteria decision making technique

    Fuzzy clustering method and evaluation based on multi criteria decision making technique by Sameer, Fadhaa Othman

    Published 2018
    “…For the third problem a modified of Kohonen Network (MKN) algorithm was proposed to select the initial centres of clusters. …”
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  16. 16
    MuDi-Stream: A multi density clustering algorithm for evolving data stream

    MuDi-Stream: A multi density clustering algorithm for evolving data stream by Amini, A., Saboohi, H., Herawan, T., Teh, Y.W.

    Published 2016
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  17. 17
    A green clustering protocol for mobile sensor network using particle swarm optimization

    A green clustering protocol for mobile sensor network using particle swarm optimization by Latiff, N.M.A., NikAbdMalik, N., Latiff, A.H.A.

    Published 2016
    “…In this paper, we propose a green clustering protocol for mobile sensor networks using particle swarm optimization (PSO) algorithm. …”
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  18. 18
    A green clustering protocol for mobile sensor network using particle swarm optimization

    A green clustering protocol for mobile sensor network using particle swarm optimization by Latiff, N.M.A., NikAbdMalik, N., Latiff, A.H.A.

    Published 2016
    “…In this paper, we propose a green clustering protocol for mobile sensor networks using particle swarm optimization (PSO) algorithm. …”
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  19. 19
    Energy Efficient LEACH (EE-LEACH) Routing Algorithm for Wireless Sensor Networks

    Energy Efficient LEACH (EE-LEACH) Routing Algorithm for Wireless Sensor Networks by Pillay, Kosheila Sundram

    Published 2019
    “…This reduces the re-election and re-clustering processes which lowers the network energy consumption. …”
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  20. 20
    Topological Clustering via Adaptive Resonance Theory With Information Theoretic Learning

    Topological Clustering via Adaptive Resonance Theory With Information Theoretic Learning by Masuyama, Naoki, Loo, Chu Kiong, Ishibuchi, Hisao, Kubota, Naoyuki, Nojima, Yusuke, Liu, Yiping

    Published 2019
    “…Other types of the ART-based topological clustering algorithms have been developed, however, these algorithms have various drawbacks such as a large number of parameters, sensitivity to noisy data. …”
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