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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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The comprehensive evaluation of innovative education quality from the perspective of balanced and stable development

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INTERNATIONAL JOURNAL OF INNOVATION AND SUSTAINABLE DEVELOPMENT 2025年第1期19卷 43-57页

作者： Feng, Xiaoping Hunan City Univ Coll Humanities Yiyang 413000 Hunan Peoples R China

In order to overcome the problems of long data collection time, high error rate of index weight calculation and low accuracy of traditional evaluation methods, a comprehensive evaluation method of innovative education quality from the perspective of balanced and stable development is proposed. Firstly, the comprehensive evaluation index of innovative education quality under the background of balanced and stable development is preliminarily determined. The k-means clustering algorithm is used to collect education data, and the data are fused and reduced. Then, the comprehensive evaluation system of innovative education quality is established. Finally, the weight of evaluation index is calculated by AHP, and the comprehensive evaluation of education quality is realised by three-level fuzzy comprehensive evaluation model. The experimental results show that the average data acquisition time of this method is 0.63 s, the maximum error rate of evaluation index weight calculation is 2%, and the evaluation accuracy is above 94%.

关键词： balanced and stable development innovative education comprehensive quality evaluation k-means clustering algorithm reduction processing three-level fuzzy comprehensive evaluation

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Feature Selection algorithm Based on k-means clustering 7

Feature Selection Algorithm Based on K-means Clustering

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7th IEEE Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER)

作者： Tang, Xue Dong, Min Bi, Sheng Pei, Maofeng Cao, Dan Xie, Cheche Chi, Sunhuang South China Univ Technol Sch Comp Sci & Engn Guangzhou 510640 Guangdong Peoples R China

ISBN: (纸本)9781538604908

In order to improve the performance of the feature selection algorithm, a feature selection algorithm based on k-means clustering is designed. The algorithm makes use of the idea of k-means clustering based on cosine distance to cluster the features, so that the obtained feature subset has strong correlation and no redundancy. The experimental results show that the feature selection algorithm based on k-means clustering has high efficiency for classification tasks and has short running time, so the algorithm has strong practicability for feature selection.

关键词： mechine learning feature selection k-means clustering algorithm

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Bus travel feature inference with small samples based on multi-clustering topic model over Internet of Things

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2025年 163卷

作者： Liu, Hongjie Shi, Haotian Yuan, Tengfei Fu, Sicheng Ran, Bin Xi'an Jiaotong Univ Sch Comp Sci & Technol Xian 710049 Peoples R China Univ Wisconsin Madison Civil & Environm Engn Madison WI 53706 USA Shanghai Univ SILC Business Sch Shanghai 201800 Peoples R China

With the widespread application of Internet of Things (IoT) technology, there has been a shift from a broad-brush to a more refined approach in traffic optimization. An increasing amount of IoT data is being utilized in trajectory mining and inference, offering more precise characteristic information for optimizing public transportation. Services that optimize public transit based on inferred travel characteristics can enhance the appeal of public transport, increase its likelihood as a travel choice, alleviate traffic congestion, and reduce carbon emissions. However, the inherent complexities of disorganized and unstructured public transportation data pose significant challenges to extracting travel features. This study explores the enhancement of bus travel by integrating advanced technologies like positioning systems, IoT, and AI to infer features in public transportation data. It introduces the Mk-LDA (meanshift kmeans Latent Dirichlet Allocation), a novel thematic modeling technique for deducing characteristics of public transit travel using limited travel trajectory data. The model employs a segmented inference methodology, initially leveraging the Mean-shift clustering algorithm to create POI seeds, followed by the P-k-means algorithm for discerning patterns in user travel behavior and extracting travel modalities. Additionally, a P-LDA (POI-Latent Dirichlet Allocation) inference algorithm is proposed to examine the interplay between travel characteristics and behaviors, specifically targeting attributes significantly correlated with public transit usage, including age, occupation, gender, activity levels, cost, safety, and personality traits. Empirical validation highlights the efficacy of this thematic modeling-based inference technique in identifying and predicting travel characteristics and patterns, boasting enhanced interpretability and outperforming conventional benchmarks.

关键词： Internet of Things Point of interest k-means clustering algorithm Latent dirichlet allocation Feature inference Intelligent transportation

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A Hybrid k-means Method based on Modified Rat Swarm Optimization algorithm for Data clustering 43

A Hybrid K-means Method based on Modified Rat Swarm Optimiza...

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43rd Chinese Control Conference (CCC)

作者： Lou, Taishan Guan, Guanguang Yu, Zhepeng Wang, Yu Tong, Shihao Zhengzhou Univ Light Ind Sch Elect & Informat Engn Zhengzhou 450002 Henan Peoples R China

ISBN: (纸本)9798350366907;9789887581581

The original k-means clustering algorithm is prone to local optima and sensitive to the initial clustering center, which have a great impact on accuracy and stability of clustering results in practical applications. To overcome this limitation, an innovative k-means clustering method based on modified rat swarm optimization (RSO) algorithm is proposed. A nonlinear convergence factor is introduced into the RSO to adjust convergence speed of different data sets and improve the global search ability. Then, a reverse initial population strategy is adopted to increase population diversity, thus improving the robustness of the algorithm to the initial conditions. The modified RSO algorithm is used to find the initial optimal cluster centroid, and then k-means algorithm is used to refine the optimized initial cluster centroid to improve the clustering accuracy. The experimental results show that compared with the original k-means clustering algorithm, the improved algorithm has achieved significant improvement in each index of iris, wine and glass datasets, which proves the effectiveness and superiority of the algorithm. This paper presents a new hybrid clustering algorithm which combines the improved swarm intelligent optimization algorithm with k-means clustering algorithm. This method effectively solves the problem that k-means clustering algorithm is sensitive to the initial cluster center.

关键词： k-means clustering algorithm Rat Swarm Optimization global search ability

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k-means clustering Based on Improved Quantum Particle Swarm Optimization algorithm 13

K-means Clustering Based on Improved Quantum Particle Swarm ...

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13th International Conference on Advanced Computational Intelligence (ICACI)

作者： Bai, Lili Song, Zerui Bao, Haijie Jiang, Jingqing Inner Mongolia Univ Nationalities Coll Comp Sci & Technol Tongliao Peoples R China Inner Mongolia Univ Nationalities Coll Math & Phys Tongliao Peoples R China

ISBN: (纸本)9781665412544

In clustering, in order to find a better data clustering center, make the algorithm convergence faster and clustering results more accurate, a k-means clustering algorithm based on improved quantum particle swarm optimization algorithm is proposed. In this algorithm, the cluster center is simulated as a particle. Cloning and mutation operations are used to increase the diversity and improve the global search ability of QPSO. A suitable and stable cluster center is obtained. Finally, an effective clustering result is obtained. The algorithm is tested with UCI data set. The results show that the improved algorithm not only ensures the global convergence of the algorithm, but also obtains more accurate clustering results.

关键词： k-means clustering algorithm Quantum Particle Swarm Optimization algorithm QPSO-k-means algorithm Cloning Mutation

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Customer segmentation using k-means clustering and the adaptive particle swarm optimization algorithm

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APPLIED SOFT COMPUTING 2021年第PartB期113卷 107924-107924页

作者： Li, Yue Chu, Xiaoquan Tian, Dong Feng, Jianying Mu, Weisong China Agr Univ Coll Informat & Elect Engn Beijing 100083 Peoples R China Minist Agr Key Lab Viticulture & Enol Beijing 100083 Peoples R China

The improvement of enterprise competitiveness depends on the ability to match segmented customers in a competitive market. In this study, we propose a customer segmentation method based on the improved k-means algorithm and the adaptive particle swarm optimization (PSO) algorithm. The current PSO algorithm can easily fall into a local extremum;thus, adaptive learning PSO (ALPSO) is proposed to improve the optimization accuracy. On the basis of the analysis of population-based optimization, the inertia weight, learning factors, and the position update method are redesigned. To prevent the k-means clustering algorithm from depending on initial cluster centres, the ALPSO algorithm is used to optimize the k-means cluster centres (kM-ALPSO). Aimed at the issue of clustering the actual grape-customer consumption mixed dataset, factor analysis is used to extract numerical variables. We then propose a dissimilarity measurement method to cluster the mixed data. We compare ALPSO with several parameter update methods. We also conduct comparative experiments to compare kM-ALPSO on five UCI datasets. Finally, the improved kM-ALPSO (IkM-ALPSO) clustering algorithm is applied in customer segmentation. All results show that the three proposed methods outperform existing models. The experimental results also demonstrate the effectiveness and practicability of IkM-ALPSO for customer segmentation. (C) 2021 Elsevier B.V. All rights reserved.

关键词： k-means clustering algorithm Particle swarm optimization algorithm Adaptive parameter learning Mixed data Customer segmentation

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A new data-adaptive network design methodology based on the k-means clustering and modified ISODATA algorithm for regional gravity field modeling via spherical radial basis functions

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JOURNAL OF GEODESY 2022年第11期96卷 91-91页

作者： Ulug, Rasit karslioglu, Mahmut Onur Middle East Tech Univ Dept Geodet & Geog Informat Technol TR-068000 Ankara Turkey Middle East Tech Univ Dept Civil Engn Geomat Engn Div Ankara Turkey

In this study, a new data-adaptive network design methodology called k-SRBF is presented for the spherical radial basis functions (SRBFs) in regional gravity field modeling. In this methodology, the cluster centers (centroids) obtained by the k-means clustering algorithm are post-processed to construct a network of SRBFs by replacing the centroids with the SRBFs. The post-processing procedure is inspired by the heuristic method, Iterative Self-Organizing Data Analysis Technique (ISODATA), which splits clusters within the user-defined criteria to avoid over- and under-parameterization. These criteria are the minimum spherical distance between the centroids and the minimum number of samples for each cluster. The bandwidth (depth) of each SRBF is determined using the generalized cross-validation (GCV) technique in which only the observations within the radius of impact area (RIA) are used. The numerical tests are carried out with real and simulated data sets to investigate the effect of the user-defined criteria on the network design. Different bandwidth limits are also examined, and the appropriate lower and upper bandwidth limits are chosen based on the empirical signal covariance function and user-defined criteria. Also, additional tests are performed to verify the performance of the proposed methodology in combining different types of observations, such as terrestrial and airborne data available in Colorado. The results reveal that k-SRBF is an effective methodology to establish a data-adaptive network for SRBFs. Moreover, the proposed methodology improves the condition number of normal equation matrix so that the least-squares procedure can be applied without regularization considering the user-defined criteria and bandwidth limits.

关键词： Regional gravity field modeling Spherical radial basis functions Data-adaptive network design Poisson kernel k-means clustering algorithm ISODATA

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A Hybrid k-means Method based on Modified Rat Swarm Optimization algorithm for Data clustering

A Hybrid K-means Method based on Modified Rat Swarm Optimiza...

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第43届中国控制会议

作者： Taishan Lou Guanguang Guan Zhepeng Yue Yu Wang Shihao Tong School of Electrical and Information Engineering Zhengzhou University of Light Industry

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

The original k-means clustering algorithm is prone to local optima and sensitive to the initial clustering center,which have a great impact on accuracy and stability of clustering results in practical *** overcome this limitation,an innovative k-means clustering method based on modified rat swarm optimization(RSO) algorithm is proposed.A nonlinear convergence factor is introduced into the RSO to adjust convergence speed of different data sets and improve the global search ***,a reverse initial population strategy is adopted to increase population diversity,thus improving the robustness of the algorithm to the initial *** modified RSO algorithm is used to find the initial optimal cluster centroid,and then k-means algorithm is used to refine the optimized initial cluster centroid to improve the clustering *** experimental results show that compared with the original k-means clustering algorithm,the improved algorithm has achieved significant improvement in each index of iris,wine and glass datasets,which proves the effectiveness and superiority of the *** paper presents a new hybrid clustering algorithm which combines the improved swarm intelligent optimization algorithm with k-means clustering *** method effectively solves the problem that k-means clustering algorithm is sensitive to the initial cluster center.

关键词： k-means clustering algorithm Rat Swarm Optimization global search ability

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An Improved clustering algorithm Based on k-means and Artificial Bee Colony Optimization for Datasets that Contain Outliers

An Improved Clustering Algorithm Based on k-Means and Artifi...

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International Conference on Computing, Power and Communication Technologies

作者： Anu Balachandran k.A. Abdul Nazeer Department of Computer Science and Engineering National Institute of Technology

ISBN: (纸本)9781538644928;9781538644911

k-means clustering algorithm is the most widely used algorithm in clustering. It is most popular because of its simplicity. There are a lot of issues faced by k-means algorithm such as, low quality of clusters formed, inability to detect outliers and solutions that can be local optimal solution. In this paper a simple outlier detection algorithm that makes use of Mean and Standard Deviation, is applied on datasets. These datasets are then given as input to an already existing hybrid clustering algorithm of k-means and Artificial Bee Colony (ABC) algorithm. By applying the outlier detection algorithm, it ensures that the clusters formed are of better quality.

关键词： clustering Data mining k-means clustering algorithm Artificial bee colony (ABC) algorithm Outlier detection ARI score

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