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A proposed hybrid clustering algorithm using K-means and BIRCH for cluster based cab recommender system (CBCRS)

International Journal of Information Technology (Singapore) 2023年第1期15卷 219-227页

作者： Mann, Supreet Kaur Chawla, Sonal Panjab University Panjab Chandigarh India

An efficient Cluster Based Cab Recommender System (CBCRS) assists the cab drivers with the recommendations about passenger pickup location available at the shortest distance from him. To recommend drivers about the passenger pickup location, one need to group the Global Positioning System (GPS) coordinates of several pickup points of the same geographic region. The GPS coordinates of cab pick-up points are unsupervised data. clustering of unsupervised cab dataset is troublesome since cab dataset is a large database and clustering techniques when applied on such large datasets do not generate good clusters for GPS datapoints. Therefore, this research paper proposes an improved hybrid clustering algorithm which combines the features of Partition-based clustering and Hierarchical Based clustering techniques. Thus, the objectives of the research paper are four folds: firstly, the research paper identifies various clustering techniques to cluster GPS Coordinates. Secondly, to design and develop an improved hybrid clustering algorithm for CBCRS. Thirdly, the research paper compares the clusters formed by the proposed algorithm with standard K-Means and Balanced Iterative Reducing and clustering using Hierarchies (BIRCH) using three datasets over Silhouette Coefficient and Calinski-Harabasz Score. Finally, the paper concludes and analyses the results of the proposed algorithm. © 2022, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management.

关键词： BIRCH clustering algorithm Calinski-Harabasz score hybrid clustering algorithm K-Means clustering algorithm Recommender system Silhouette score Unsupervised machine learning

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A hybrid clustering algorithm for multiple-source resolving in bioluminescence tomography

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JOURNAL OF BIOPHOTONICS 2018年第4期11卷 e201700056-e201700056页

作者： Guo, Hongbo Yu, Jingjing Hu, Zhenhua Yi, Huangjian Hou, Yuqing He, Xiaowei Northwest Univ Xian Sch Informat Sci & Technol Xian 710069 Shaanxi Peoples R China Shaanxi Normal Univ Sch Phys & Informat Technol Xian 710119 Shaanxi Peoples R China Chinese Acad Sci Inst Automat Key Lab Mol Imaging Beijing Peoples R China

Bioluminescence tomography is a preclinical imaging modality to locate and quantify internal bioluminescent sources from surface measurements, which experienced rapid growth in the last 10 years. However, multiple-source resolving remains a challenging issue in BLT. In this study, it is treated as an unsupervised pattern recognition problem based on the reconstruction result, and a novel hybrid clustering algorithm combining the advantages of affinity propagation (AP) and K-means is developed to identify multiple sources automatically. Moreover, we incorporate the clustering analysis into a general multiple-source reconstruction framework, which can provide stable reconstruction and accurate resolving result without providing the number of targets. Numerical simulations and in vivo experiments on 4T1-luc2 mouse model were conducted to assess the performance of the proposed method in multiple-source resolving. The encouraging results demonstrate significant effectiveness and potential of our method in preclinical BLT applications.

关键词： bioluminescence tomography hybrid clustering algorithm in vivo optical imaging multiple-source resolving

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Cluster-based decision making: a novel approach for handling large-scale alternatives in rooftop solar PV site selection

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ARTIFICIAL INTELLIGENCE REVIEW 2025年第4期58卷 1-45页

作者： Sharma, Priya Mehlawat, Mukesh Kumar Gupta, Pankaj Pamucar, Dragan Univ Delhi Dept Operat Res Delhi 110007 India Univ Belgrade Fac Org Sci Dept Operat Res & Stat Belgrade Serbia Yuan Ze Univ Coll Engn Taoyuan City Taiwan Korea Univ Dept Appl Math Sci Coll Sci & Technol Sejong 30019 South Korea

This research proposes to develop a systematic methodology for ranking a large number of alternatives in large-scale real-world problems under uncertainty, where the traditional individual ranking methods fail to provide meaningful and actionable insights. This paper introduces a novel framework for fuzzy large-scale decision-making (FLSDM) using triangular neutrosophic fuzzy numbers (TNFNs) to perform cluster-based ranking as a solution to these challenges. The proposed approach develops the Sugeno-Weber operator within the TNFN environment for data aggregation. An advanced K-means++ algorithm is designed to enable precise clustering of alternatives. Using the TOPSIS and DEA cross-efficiency model, extended for TNFNs, the clusters are ranked, and the alternatives are prioritized within each cluster. The practical use of the proposed approach is demonstrated through a real-world case study on rooftop solar photovoltaic (PV) site selection. Additionally, thorough analyses are conducted to validate its robustness and effectiveness.

关键词： Fuzzy large-scale decision-making Multi-criteria optimization Triangular neutrosophic fuzzy number Sugeno-Weber aggregation operator hybrid clustering algorithm Rooftop solar PV site selection

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Selecting a health emergency strategy through large-scale multi-criteria decision-making based on intuitionistic fuzzy self-confidence data

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APPLIED SOFT COMPUTING 2025年 176卷

作者： Sharma, Priya Mehlawat, Mukesh Kumar Gupta, Pankaj Verma, Shilpi Univ Delhi Dept Operat Res Delhi 110007 India

In complex decision-making scenarios involving multiple stakeholders, the uncertainty and individual confidence of decision-makers (DMs) are crucial in determining the outcomes. A novel approach is proposed in this paper to improve decision-making processes within a large group of DMs operating under an "Intuitionistic Fuzzy Self-Confidence (IFN-SC)" setting. The research presents a hybrid clustering algorithm to categorize DMs based on their numerical similarities and psychological factors. A multi-objective nonlinear optimization problem is employed to determine the criteria weights in the IFN-SC environment when the weight vector is either partially or fully unknown. Using the max operator, we derive a single-objective nonlinear optimization problem, which is solved by the "Particle Swarm Optimization (PSO)" algorithm. Furthermore, extending the "Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS)" for the IFN-SC environment significantly enhances the model's effectiveness in ranking alternatives. The study exemplified its capability in managing a large-scale decision-making problem based on health emergency strategy selection and presented various analyses highlighting its utility, adaptability, and robustness in practical situations.

关键词： Large-scale multi-criteria decision-making Intuitionistic fuzzy self-confidence number hybrid clustering algorithm Particle swarm optimization Health emergency strategy selection

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Application of a Kernel Method Autoencoder-Based clustering Approach Combining DBSCAN and K-means for Rail Transit Station Classification 6

Application of a Kernel Method Autoencoder-Based Clustering ...

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6th International Conference on Frontier Technologies of Information and Computer, ICFTIC 2024

作者： Zhang, Xuebiao Chen, Jia Zhang, Lidong Zhai, Yingdong School of Railway Transportation Shanghai Institute of Technology Shanghai China Shanghai Inesa Network Co. Ltd. Department of Railway Transportation Shanghai China Technology Center Shanghai Shentong Metro Group Co. Ltd. Shanghai China

ISBN: (纸本)9798331541750

With the rapid development of rail transit, station management faces increasing complexity and an explosive growth in data volume. To address the challenges posed by the high dimensionality and diversity of passenger flow data, this study proposes an enhanced clustering model that integrates KAE (Kernel method-based Autoencoder, KAE) for dimensionality reduction with a hybrid clustering approach combining DBSCAN and K-Means. The KAE is utilized to reduce the dimensionality of the high-dimensional data while retaining key features, which improves the effectiveness of the subsequent clustering process. In this model, DBSCAN is applied to identify high-density regions in the data, which are then used to initialize the K-Means algorithm. This ensures that the initial centroids are positioned in areas with high data density. The proposed approach combines the capability of DBSCAN to detect clusters of arbitrary shapes based on density with the ability of K-Means to refine cluster centroids for better convergence. The use of KAE further enhances the clustering performance by optimizing the feature space, making it more suitable for clustering. Experiments conducted on both rail transit and general datasets demonstrate that the proposed model achieves superior clustering performance compared to traditional methods. The results show improved classification accuracy and robustness, confirming the model's strong generalization capability across different types of data. This method provides an efficient and scalable solution for the classification of rail transit stations and offers new insights for managing and analyzing high-dimensional data in various applications. © 2024 IEEE.

关键词： component dbscan hybrid clustering algorithm k-means kernel method autoencoder urban rail transit

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Use of hybrid clustering and Scattering Parameters for Liquid Classification

ELECTRICA

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ELECTRICA 2022年第2期22卷 237-243页

作者： Efeoglu, Ebru Tuna, Gurkan Kutahya Dumlupinar Univ Dept Software Engn Kutahya Turkey Trakya Univ Dept Comp Programming Edirne Turkey

With the advancement of technology, the use of machine learning techniques has increased. The need for the prevention of terrorist attacks has brought upon the use of machine learning techniques to explosive detection. Flammable liquids such as alcohol are easily available and widely used in various terrorist attacks. In this study, a new microwave measurement system is developed and a hybrid clustering approach is proposed to classify liquids. With the proposed measurement system, the reflection coefficient (S-11 parameter) of liquids in bottles is measured at room temperature and these measurements are used as inputs by the proposed clustering algorithm. The results obtained using the proposed clustering algorithm are compared with the results obtained using a set of well-known clustering algorithms, that is, K-means, hierarchical clustering, farthest first, and fuzzy C-means, in order to make a fair comparison. The results show that the proposed clustering algorithm provides 100% accuracy and is superior to the well-known algorithms used in this study. The results will enable us to manufacture a low-cost liquid scanner for railway stations and shopping malls as well as small airports. The proposed liquid scanner's design was completed, and the manufacturing phase has been started.

关键词： Accuracy explosive liquids flammable liquids hybrid clustering algorithm liquid classification

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Rough intuitionistic type-2 fuzzy c-means clustering algorithm for MR image segmentation

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IET IMAGE PROCESSING 2019年第4期13卷 607-614页

作者： Chen, Xiangjian Li, Di Wang, Xun Yang, Xibei Li, Hongmei Jiangsu Univ Sci & Technol Sch Comp Sci & Engn Zhenjiang Jiangsu Peoples R China China Shipbldg Ind Corp 723 Yangzhou Jiangsu Peoples R China

In recent years, the clinical application of magnetic resonance (MR) images is more and more extensive and in-depth. However, image segmentation is a bottleneck to restrict the application of MR imaging in clinic, and the segmentation of brain MR images now is confronted with the presence of uncertainty and noise, and various kinds of algorithms have been proposed to handle this problem. In this study, a hybrid clustering algorithm combined with a new intuitionistic fuzzy factor and local spatial information is proposed, where type-2 fuzzy logic can handle randomness, the rough set can deal with vagueness, and the intuitionistic fuzzy logic can address the external noises. Finally, the experimental tests have been done to demonstrate the superiority of the proposed technique.

关键词： image segmentation fuzzy set theory fuzzy logic pattern clustering extensive depth in-depth bottleneck MR imaging clinic brain MR images uncertainty noise hybrid clustering algorithm intuitionistic fuzzy factor local spatial information type-2 fuzzy logic rough set intuitionistic fuzzy logic external noises rough intuitionistic type-2 MR image segmentation clinical application magnetic resonance images

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A Comparative Investigation of Similarity Coefficients Applied to the Cell Formation Problem using hybrid clustering algorithms

A Comparative Investigation of Similarity Coefficients Appli...

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International Conference on Materials Manufacturing and Modelling (ICMMM)

作者： Pachayappan, Murugaiyan Panneerselvam, Ramasamy Pondicherry Univ Sch Management Dept Int Business Pondicherry 605014 India Pondicherry Univ Sch Management Dept Management Studies Pondicherry 605014 India

Cellular manufacturing system plays a vital role in improving organizational productivity. The efficiency and effectiveness of machine-component cell formation of cellular manufacturing system are measured in terms of grouping efficiency and grouping efficacy, respectively. These measures are affected by inputs as well as methods used to form the machine-component cells. The input may be in the form of 0-1 matrix, which is called as machine-component incidence matrix or in terms of similarity coefficients among machines as well as among components. In this paper, the machine-component cell formation problem using "similarity coefficient" as an input is considered. The objective is to compare the effect of five different similarity coefficients on the grouping efficiency as well as on the grouping efficacy. Two different algorithms, viz. hybrid Principle Component Analysis and hybrid Agglomerative clustering algorithm are used to solve the machine-component cell formation problem. In each of these algorithms, Rank Order clustering algorithm is used as a local search algorithm to form the final machine-component cells. A complete factorial experiment with 10 problem sizes, five different similarity coefficients, and two different algorithms is designed with two replications under each of the experimental combinations to check the main effects of the factors and their interaction effects on the grouping measures of the machine-component cell formation problem. The inferences of the complete factorial experiment are reported. (C) 2017 Elsevier Ltd. All rights reserved. Selection and/or Peer-review under responsibility of International Conference on Materials Manufacturing and Modelling (ICMMM - 2017).

关键词： Cellular manufacturing system Cell formation problem hybrid clustering algorithm Similarity coefficient ANOVA Grouping efficiency Grouping efficacy Design of experiment

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Recognition for multiple sources of Bioluminescence tomography: a comparative study 10

Recognition for multiple sources of Bioluminescence tomograp...

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10th International Conference on Information Optics and Photonics

作者： Yi, Huangjian Hu, Yuelin Jiao, Pu Cao, Xin Hou, Yuqing Zhao, Fengjun He, Xiaowei Northwest Univ Sch Informat Sci & Technol Xian 710069 Shaanxi Peoples R China

ISBN: (纸本)9781510625808

Bioluminescence tomography (BLT) can reconstruct internal bioluminescent source from the surface measurements. However, multiple sources resolving of BLT is always a challenge. In this work, a comparative study on hybrid clustering algorithm, synchronization-based clustering algorithm and iterative self-organizing data analysis technique algorithm for multiple sources recognition of BLT is conducted. Simulation experiments on two and three sources reconstruction are demonstrated the performances of these three algorithms. The results show that the iterative self-organizing data analysis technique is more suitable for the closer multiple-targets and the other two algorithms are suitable for distant targets. Moreover, iterative self-organizing data analysis technique has the least computing time.

关键词： bioluminescence tomography multiple-source resolving hybrid clustering algorithm synchronization-based clustering iterative self-organizing data analysis technique

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G-TREACLE: A new grid-based and tree-alike pattern clustering technique for large databases

G-TREACLE: A new grid-based and tree-alike pattern clusterin...

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12th Pacific-Asia Conference on Knowledge Discovery and Data Mining

作者： Tsai, Cheng-Fa Yen, Chia-Chen Natl Pingtung Univ Sci & Technol Dept Management Informat Syst Pingtung 91201 Taiwan

ISBN: (纸本)9783540681243

As data mining having attracted a significant amount of research attention, many clustering methods have been proposed in past decades. However, most of those techniques have annoying obstacles in precise pattern recognition. This paper presents a new clustering algorithm termed G-TREACLE, which can fulfill numerous clustering requirements in data mining applications. As a hybrid approach that adopts grid-based concept, the proposed algorithm recognizes the solid framework of clusters and, then, identifies the arbitrary edge of clusters by utilization of a new density-based expansion process, which named "tree-alike pattern". Experimental results illustrate that the new algorithm precisely recognizes the whole cluster, and efficiently reduces the problem of high computational time. It also indicates that the proposed new clustering algorithm performs better than several existing well-known approaches such as the K-means, DBSCAN, CLIQUE and GDH algorithms, while produces much smaller errors than the K-means, DBSCAN, CLIQUE and GDH approaches in most the cases examined herein.

关键词： data clustering data mining hybrid clustering algorithm

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