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Relevant bibliographies by topics / Hybrid clustering interference graph

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Academic literature on the topic 'Hybrid clustering interference graph'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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Journal articles on the topic "Hybrid clustering interference graph"

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Fakhar, Abbas Naveed Ur Rehman Mohammad Irshad Zahoor Irfan Jamil. "Interference Mitigation in LTE HetNet by Resource Allocation." International Journal of Engineering works 1, no. 1 (2014): 10–14. https://doi.org/10.5281/zenodo.15743.

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To provide high date rate for indoor services and communication, femtocells and microcells are planned in LTEAdvance system but main problem is how to reduce the interference between micro and femto cells and in the middle of the femtocells. In this paper we proposed regional Average channel state (RACS) to estimate the influence of interference and then we proposed hybrid clustering based on interference graph (HCIG) to reduce interference between femtocells and microcells. Based on the Results our scheme is given to reduce the interference and improve the spectrum efficiency

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Zhang, Hong. "Graph Based Hybrid Clustering With Unbounded Regions." Journal of Computer Science Applications and Information Technology 2, no. 2 (2017): 1–5. http://dx.doi.org/10.15226/2474-9257/2/2/00113.

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Wen, Zichen, Yawen Ling, Yazhou Ren, et al. "Homophily-Related: Adaptive Hybrid Graph Filter for Multi-View Graph Clustering." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 14 (2024): 15841–49. http://dx.doi.org/10.1609/aaai.v38i14.29514.

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Recently there is a growing focus on graph data, and multi-view graph clustering has become a popular area of research interest. Most of the existing methods are only applicable to homophilous graphs, yet the extensive real-world graph data can hardly fulfill the homophily assumption, where the connected nodes tend to belong to the same class. Several studies have pointed out that the poor performance on heterophilous graphs is actually due to the fact that conventional graph neural networks (GNNs), which are essentially low-pass filters, discard information other than the low-frequency inform

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Chen, Yan, and Qin Zhou Niu. "Hybrid Clustering Algorithm Based on KNN and MCL." Applied Mechanics and Materials 610 (August 2014): 302–6. http://dx.doi.org/10.4028/www.scientific.net/amm.610.302.

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MCL is a graph clustering algorithm. With the characteristics of the MCL computational process, MCL is prone to producing small clustering and separating edge nodes from the group. A hybrid clustering based on MCL combined with KNN algorithm is proposed. Hybrid algorithm improves the quality of clustering by reclassification of elements in small clustering by using KNN classification characteristics and Clustering tables required by MCL clustering. Experiment proves the improved algorithm can enhance the quality of clustering.

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Farhan, Naureen, Safdar Rizvi, Amna Shabbir, and Imran Memon. "Clustering Approaches for Efficient Radio Resource Management in Heterogeneous Networks." VFAST Transactions on Software Engineering 9, no. 3 (2021): 68–77. http://dx.doi.org/10.21015/vtse.v9i3.641.

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5G telecommunication industry promises to manage and accomplish the massive data traffic and growing network requirement complexities in heterogeneous networks (HetNets). HetNets are K-tier networks and are expected to be seamlessly connected networks with robust services for users anywhere at any time. In near future, the significance of 5G/B5G cellular networks; in both indoor and outdoor environments will be greater than before and it would add up to an exhaustive level. However, as a result of the increased density of networks, a rise in interference within these ultra-dense networks (UDN)

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Wang, Ying, and Qi Zhu. "Joint Spectrum and Power Allocation for Femtocell Based on Graph and Game Theory." Advanced Materials Research 989-994 (July 2014): 2051–56. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.2051.

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In order to mitigate the downlink interference in two-tier femtocell networks, a joint spectrum and power allocation algorithm based on graph and game theory is proposed in this paper. On the premise of ensuring the QoS (Quality of Service) of MUEs (microcell user equipments), we first adopt graph-based clustering algorithm to assign subbands to femtocells. Then, for femtocells of each cluster, their transmitting power is redistributed based on the non-cooperative game theory to further reduce the interference among them. Simulation results show that this algorithm can further improves the per

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Masoud, Mohammad Z., Yousef Jaradat, Ismael Jannoud, and Mustafa A. Al Sibahee. "A hybrid clustering routing protocol based on machine learning and graph theory for energy conservation and hole detection in wireless sensor network." International Journal of Distributed Sensor Networks 15, no. 6 (2019): 155014771985823. http://dx.doi.org/10.1177/1550147719858231.

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In this work, a new hybrid clustering routing protocol is proposed to prolong network life time through detecting holes and edges nodes. The detection process attempts to generate a connected graph without any isolated nodes or clusters that have no connection with the sink node. To this end, soft clustering/estimation maximization with graph metrics, PageRank, node degree, and local cluster coefficient, has been utilized. Holes and edges detection process is performed by the sink node to reduce energy consumption of wireless sensor network nodes. The clustering process is dynamic among sensor

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Farhan, Naureen, and Safdar Rizvi. "An Interference-Managed Hybrid Clustering Algorithm to Improve System Throughput." Sensors 22, no. 4 (2022): 1598. http://dx.doi.org/10.3390/s22041598.

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In the current smart era of 5G, cellular devices and mobile data have increased exponentially. The conventional network deployment and protocols do not fulfill the ever-increasing demand for mobile data traffic. Therefore, ultra-dense networks have widely been suggested in the recent literature. However, deploying an ultra-dense network (UDN) under macro cells leads to severe interference management challenges. Although various centralized and distributed clustering methods have been used in most research work, the issue of increased interference persists. This paper proposes a joint small cel

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Kathiria*, Preeti, and Harshal Arolkar. "Document Clustering based on Phrase and Single Term Similarity using Neo4j." International Journal of Innovative Technology and Exploring Engineering 9, no. 3 (2020): 3188–92. http://dx.doi.org/10.35940/ijitee.c9050.019320.

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Document similarity generally rely on single term similarity such as cosine similarity. To achieve better document similarity, along with single term phrase- more informative feature can be used. To find out shared phrases across the corpus the Document Index graph (DIG) representation model is used. Document representation - DIG model incrementally construct the graph and simultaneously finds the shared phrase between current document and previously inserted documents from the graph. The similarity between documents is mainly depends on the number of shared phrases and single term similarity

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Park, Hyuk-Gyu, Kwang-Seong Shin, and Jong-Chan Kim. "Efficient Clustering Method for Graph Images Using Two-Stage Clustering Technique." Electronics 14, no. 6 (2025): 1232. https://doi.org/10.3390/electronics14061232.

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Graphimages, which represent data structures through nodes and edges, present significant challenges for clustering due to their intricate topological properties. Traditional clustering algorithms, such as K-means and Density-Based Spatial Clustering of Applications with Noise (DBSCAN), often struggle to effectively capture both spatial and structural relationships within graph images. To overcome these limitations, we propose a novel two-stage clustering approach that integrates conventional clustering techniques with graph-based methodologies to enhance both accuracy and efficiency. In the f

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Book chapters on the topic "Hybrid clustering interference graph"

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Van Pham, Hai, and Dong Nguyen Tien. "Hybrid Louvain-Clustering Model Using Knowledge Graph for Improvement of Clustering User’s Behavior on Social Networks." In Intelligent Systems and Networks. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2094-2_16.

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Guo, Mengli, Gang Chuai, Weidong Gao, and Yuhan Zhang. "Dynamic TDD Interference Mitigation Using Graph Theory Based Cell Clustering in 5G Ultra-Dense Network." In Lecture Notes in Electrical Engineering. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-6571-2_85.

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Banerjee, Biplab, Pradeep Kumar Mishra, Surender Varma, and Buddhiraju Krishna Mohan. "A Novel Graph Based Clustering Technique for Hybrid Segmentation of Multi-spectral Remotely Sensed Images." In Advanced Concepts for Intelligent Vision Systems. Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-02895-8_25.

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Roy, Parthajit, Swati Adhikari, and J. K. Mandal. "A Self-Organizing Map-Based Spectral Clustering on Shortest Path of a Graph." In Hybrid Intelligent Techniques for Pattern Analysis and Understanding. Chapman and Hall/CRC, 2017. http://dx.doi.org/10.1201/9781315154152-13.

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Chen Chun-Hsien, Huang Yuexiang, Khoo Li Pheng, and Chang Danni. "Kansei Clustering Using Design Structure Matrix and Graph Decomposition for Emotional Design." In 20th ISPE International Conference on Concurrent Engineering. IOS Press, 2013. https://doi.org/10.3233/978-1-61499-302-5-91.

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Conventionally, Kansei engineering relies heavily on the intuition of the person who uses the method in clustering the Kansei. As a result, the selection of Kansei adjectives may not be consistent with the consumer's opinions. Nevertheless, to obtain a consumer-consistent result, all of the collected Kansei adjectives (usually hundreds) might need to be evaluated by every survey participant, which is impractical in most design cases. Accordingly, a Kansei clustering method based on design structure matrix (DSM) and graph decomposition (GD) is proposed in this work. The method breaks the Kansei

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Veerappan, Kousik Nalliyanna Goundar, Yuvaraj Natarajan, Arshath Raja, Jeyaprabhavathi Perumal, and S. Jerald Nirmal Kumar. "Categorical Data Clustering Using Meta Heuristic Link-Based Ensemble Method." In Advances in Healthcare Information Systems and Administration. IGI Global, 2023. http://dx.doi.org/10.4018/978-1-6684-6894-4.ch012.

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Conventional ensemble clustering is a consensus function that fails to produce final clusters. Such poor clusters partitioning creates poor stability with reduced clustering accuracy. This motivates to improve the final clustering quality using a hybrid ensemble-based model. In this study, an optimized link-based ensemble clustering approach is proposed to refine the incomplete datasets and to refine unknown entries in categorical dataset. The proposed work uses link-based similarity measure to find the availability of unknown datasets from link network of clusters. The ensemble clustering gen

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Fazio, Peppino, Mauro Tropea, Salvatore Marano, and Vincenzo Curia. "A Hybrid Complex Network Model for Wireless Sensor Networks and Performance Evaluation." In Advanced Methods for Complex Network Analysis. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-9964-9.ch016.

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This chapter proposes a new approach, based on Complex Networks modeling, to alleviate the limitations of wireless communications. In fact, many recent studies have demonstrated that telecommunication networks can be well modeled as complex ones, instead of using the classic approach based on graph theory. The study of Complex Networks is a young and active area of scientific research, inspired largely by the empirical study of real-world networks, such as computers and social networks. The chapter contributes to the improvement of distributed communication, quantifying it in terms of clusteri

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Saeed, Soobia, Habibullah Bin Haroon, Mehmood Naqvi, Noor Zaman Jhanjhi, Muneer Ahmad, and Loveleen Gaur. "A Systematic Mapping Study of Low-Grade Tumor of Brain Cancer and CSF Fluid Detecting Approaches and Parameters." In Approaches and Applications of Deep Learning in Virtual Medical Care. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-7998-8929-8.ch010.

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Low-grade tumor or CSF fluid, the symptoms of brain tumor and CSF liquid, usually require image segmentation to evaluate tumor detection in brain images. This research uses systematic literature review (SLR) process for analysis of the different segmentation approach for detecting the low-grade tumor and CSF fluid presence in the brain. This research work investigated how to evaluate and detect the tumor and CSF fluid, improve segmentation method to detect tumor through graph cut hidden markov model of k-mean clustering algorithm (GCHMkC) techniques and parameters, extract the missing values i

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Conference papers on the topic "Hybrid clustering interference graph"

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Nji, Francis Ndikum, Omar Faruque, Mostafa Cham, Janeja Vandana, and Jianwu Wang. "Hybrid Ensemble Deep Graph Temporal Clustering for Spatiotemporal Data." In 2024 IEEE International Conference on Big Data (BigData). IEEE, 2024. https://doi.org/10.1109/bigdata62323.2024.10825871.

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Su, Hongjun, and Hong Zhang. "Hybrid Clustering Based on a Graph Model." In 2016 9th International Symposium on Computational Intelligence and Design (ISCID). IEEE, 2016. http://dx.doi.org/10.1109/iscid.2016.1062.

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Wei, Lai, and Mingyuan Xi. "Subspace Clustering with A Hybrid Adaptive Graph Filter." In ICMR '24: International Conference on Multimedia Retrieval. ACM, 2024. http://dx.doi.org/10.1145/3652583.3658042.

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Yuan, Tianzhu, Xuanli Wu, and Jiuning Zhang. "Interference graph based knapsack clustering algorithm in ultra-dense networks." In 2022 IEEE MTT-S International Wireless Symposium (IWS). IEEE, 2022. http://dx.doi.org/10.1109/iws55252.2022.9977618.

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Li, Hongjia, Xiaodong Xu, Dan Hu, Xiqiang Qu, Xiaofeng Tao, and Ping Zhang. "Graph Method Based Clustering Strategy for Femtocell Interference Management and Spectrum Efficiency Improvement." In 2010 6th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM). IEEE, 2010. http://dx.doi.org/10.1109/wicom.2010.5601236.

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Ebrahimi, Javid, and Mohammad Saniee Abadeh. "A hybrid genetic algorithm and coordinate descent optimization for graph clustering." In 2012 16th CSI International Symposium on Artificial Intelligence and Signal Processing (AISP). IEEE, 2012. http://dx.doi.org/10.1109/aisp.2012.6313766.

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Hu, Chenwu, Long Suo, Hongyan Li, Yijun Lu, and Fei Liu. "Parallel Clustering for Interference Alignment with Max-Min Fairness in Hybrid Data Center Networks." In 2021 7th International Conference on Computer and Communications (ICCC). IEEE, 2021. http://dx.doi.org/10.1109/iccc54389.2021.9674667.

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Merah, Malha, Zibouda Aliouat, and Mohamed Sofiane Batta. "A Hybrid Neural Network and Graph Theory based Clustering Protocol for Dynamic IoT Networks." In 2022 International Conference on Advanced Aspects of Software Engineering (ICAASE). IEEE, 2022. http://dx.doi.org/10.1109/icaase56196.2022.9931583.

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Rodrigues, Natan S., and Celia G. Ralha. "A Hybrid Machine Learning Method to Author Name Disambiguation." In Simpósio Brasileiro de Tecnologia da Informação e da Linguagem Humana. Sociedade Brasileira de Computação, 2024. https://doi.org/10.5753/stil.2024.245440.

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Digital bibliographic repositories, including publications, authors, and research fields are essential for sharing scientific information. Nevertheless, the information retrieval, extraction, and classification efficiency in such archives is threatened by author name ambiguity. This paper addresses the Author Name Disambiguation (AND) problem by proposing a hybrid machine learning method integrating Bidirectional Encoder Representations from Transformers (BERT), Graph Convolutional Network (GCN), and Graph Enhanced Hierarchical Agglomerative Clustering (GHAC) approaches. The BERT model extract

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De Sousa Silva, Diogo Vinícius, and Frederico Araújo Durão. "A Hybrid Approach to Recommend Long Tail Items." In XXIV Simpósio Brasileiro de Sistemas Multimídia e Web. Sociedade Brasileira de Computação - SBC, 2018. http://dx.doi.org/10.5753/webmedia.2018.4550.

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Techniques in recommendation systems generally focuses on recommending the most important items for a user. The purpose of this work is to generate recommendations focusing on long tail items, and then to conduct the user to less popular items. However, such items are of great relevance to the user. Two techniques from the literature were applied in this study in a hybrid way. The first technique is through markov chains to calculate node similarity of a user item graph. The second technique applies clustering, where items are separated into distinct clusters: popular items (short tail) and no

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