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Journal articles on the topic 'Bisecting K'

Author: Grafiati

Published: 4 June 2025

Last updated: 15 July 2025

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Puspitasari, Novianti, Joan Angelina Widians, and Noval Bayu Setiawan. "Customer segmentation using bisecting k-means algorithm based on recency, frequency, and monetary (RFM) model." Jurnal Teknologi dan Sistem Komputer 8, no. 2 (2019): 78–83. http://dx.doi.org/10.14710/jtsiskom.8.2.2020.78-83.

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Information on customer loyalty characteristics in a company is needed to improve service to customers. A customer segmentation model based on transaction data can provide this information. This study used parameters from the recency, frequency, and monetary (RFM) model in determining customer segmentation and bisecting k-means algorithm to determine the number of clusters. The dataset used 588 sales transactions for PT Dinar Energi Utama in 2017. The clusters formed by the bisecting k-means and k-means algorithm were tested using the silhouette coefficient method. The bisecting k-means algorithm can form the best customer segmentation into three groups, namely Occasional, Typical, and Gold, with a silhouette coefficient of 0.58132.

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Gohzali, Hernawati, and Dita Maria Panjaitan. "Movie Recommendation System Model using Bisecting K-Means Technique and Collaborative Filtering." Journal of Multimedia Trend and Technology 3, no. 2 (2024): 95–104. http://dx.doi.org/10.35671/jmtt.v3i2.71.

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In the current film industry, the competition is very big. We can see it in online streaming content through the ratings obtained. Film itself is a visual work that is packaged as a product of public entertainment for a specific purpose. However, there are also many films that are considered not to meet the audience's expectations. Even the films presented are sometimes illegal or pirated films. We can also find out whether a film is recommended or not. The problem is that viewers rarely understand how to see recommendations or even provide appropriate film recommendations. This study aims to develop a film recommendation system model using a combination of K-Means bisecting and Collaborative Filtering. The film data used in this study comes from Movie-Lens which consists of 100,000 ratings from 668 users for 10329 film titles in 18 film genres. The training process consists of a cluster process with the K-Means bisecting algorithm and calculating similarity values with collaborative filtering (item-based and user-based). The testing process is carried out to calculate the system error value by calculating the Mean Absolute Error (MAE) value. The results of the study show that recommendations with bisecting K-Means and user-based collaborative filtering get lower MAE values compared to bisecting K-Means and item-based collaborative filtering.

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Hisyam, Masfu, Ali Ridho Barakbah, Iwan Syarif, and Ferry Astika S. "Spatio Temporal with Scalable Automatic Bisecting-Kmeans for Network Security Analysis in Matagaruda Project." EMITTER International Journal of Engineering Technology 7, no. 1 (2019): 83–104. http://dx.doi.org/10.24003/emitter.v7i1.340.

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Internet attacks are a frequent occurrence and the incidence is always increasing every year, therefore Matagaruda project is built to monitor and analyze internet attacks using IDS (Intrusion Detection System). Unfortunately, the Matagaruda project has lacked in the absence of trend analysis and spatiotemporal analysis. It causes difficulties to get information about the usual seasonal attacks, then which sector is the most attacked and also the country or territory where the internet attack originated. Due to the number of unknown clusters, this paper proposes a new method of automatic bisecting K-means with the average of SSE is 93 percents better than K-means and bisecting K-means. The usage of big spark data is highly scalable for processing massive data attack.

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Patil, Ruchika, and Amreen Khan. "Bisecting K-Means for Clustering Web Log data." International Journal of Computer Applications 116, no. 19 (2015): 36–41. http://dx.doi.org/10.5120/20448-2799.

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Li, Yanjun, and Soon M. Chung. "Parallel bisecting k-means with prediction clustering algorithm." Journal of Supercomputing 39, no. 1 (2007): 19–37. http://dx.doi.org/10.1007/s11227-006-0002-7.

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Dwididanti, Shinta, and Dimas Aryo Anggoro. "Analisis Perbandingan Algoritma Bisecting K-Means dan Fuzzy C-Means pada Data Pengguna Kartu Kredit." Emitor: Jurnal Teknik Elektro 22, no. 2 (2022): 110–17. http://dx.doi.org/10.23917/emitor.v22i2.15677.

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Di era digital seperti sekarang ini memiliki kartu kredit merupakan suatu hal yang wajar di masyarakat, dengan segala kemudahan yang ditawarkan dalam setiap transaksi pembayaran tidak menutup kemungkinan untuk menarik minat masyarakat dalam menggunakan kartu kredit. Dengan minat masyarakat yang tinggi terhadap kartu kredit, hal ini dapat dijadikan sebagai indikator yang baik bagi perusahaan kartu kredit untuk mengembangkan bisnis kartu kredit. Dalam rangka memenuhi kebutuhan konsumen akan kartu kredit, perusahaan dituntut untuk mengambil keputusan dalam menentukan strategi pemasaran yang tepat sehingga dapat menarik minat para pelanggan, salah satu caranya adalah dengan melakukan segmentasi pelanggan dengan metode clustering. Bisecting K-Means dan Fuzzy C-Means merupakan algoritma clustering yang akan digunakan pada penelitian ini untuk melakukan pengelompokan data pengguna kartu kredit. Analisis akan dilakukan untuk mengetahui algoritma dengan performa terbaik berdasarkan pengujian validitas dari kedua algoritma dengan menggunakan metode silhouette coefficient. Dari penelitian ini didapapatkan hasil bahwa Bisecting K-Means tanpa normalisasi memiliki nilai silhouette coefficient yang lebih tinggi dibandingkan dengan Fuzzy C-Means. Dimana nilai silhouette coefficient Bisecting K-Means sebesar 0.588 dan 0.579 dengan normalisasi, sedangkan nilai silhouette coefficient Fuzzy C-Means adalah 0.488 dan 0.582 dengan normalisasi.

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Phang, Elvina Lorenza. "Comparison Of Bisecting K-Means And K-Medoids Algorithm In Grouping Junior High School Students Based On Bebras Challenge 2023 Results." Prosiding Seminar Nasional KONSTELASI 2, no. 1 (2025): 20–31. https://doi.org/10.24002/prosidingkonstelasi.v2i1.11162.

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Improving the quality of education, especially Computational Thinking (CT) skills, is an important priority in the digital era. Bebras Challenge, an international competition to measure Computational Thinking. The results show the diverse abilities of Junior High School students, so that appropriate mentoring methods are needed. This study compares the Bisecting K-Means and K-Medoids algorithms to group students based on the results of the 2023 Bebras Challenge, along with their Indonesian, Mathematics, Science scores, and the duration of competition preparation. The study was conducted through data preprocessing, application of clustering algorithms, and model evaluation using Silhouette Score at the number of clusters k = 2 to k = 10, and running time. The results show two main groups, namely the first group of students with high understanding, fast processing time, and effective preparation. While the second group of students with low understanding due to less than optimal preparation. Bisecting K-Means showed the best performance with a Silhouette Score of 0.623 and an execution time of 0.005 seconds at k = 2. This study provides insights for educators and policy makers to design more effective data-based learning strategies.

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Dai, Hua, Xuelong Dai, Xiao Li, Xun Yi, Fu Xiao, and Geng Yang. "A Multibranch Search Tree-Based Multi-Keyword Ranked Search Scheme over Encrypted Cloud Data." Security and Communication Networks 2020 (January 23, 2020): 1–15. http://dx.doi.org/10.1155/2020/7307315.

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In the interest of privacy concerns, cloud service users choose to encrypt their personal data before outsourcing them to cloud. However, it is difficult to achieve efficient search over encrypted cloud data. Therefore, how to design an efficient and accurate search scheme over large-scale encrypted cloud data is a challenge. In this paper, we integrate bisecting k-means algorithm and multibranch tree structure and propose the α-filtering tree search scheme based on bisecting k-means clusters. The novel index tree is built from bottom-up, and a greedy depth first algorithm is used for filtering the nonrelevant document cluster by calculating the relevance score between the filtering vector and the query vector. The α-filtering tree can improve the efficiency without the loss of search accuracy. The experiment on a real-world dataset demonstrates the effectiveness of our scheme.

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Rana, Deepak Singh. "Generating Document Summary using Data Mining and Clustering Techniques." Mathematical Statistician and Engineering Applications 70, no. 1 (2021): 285–92. http://dx.doi.org/10.17762/msea.v70i1.2310.

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    Abstract This paper presents a novel approach to generating document summaries using data mining and clustering techniques, specifically K-means clustering and bisecting K-means clustering algorithms. With the exponential growth of textual data, there is an increasing need for efficient and accurate summarization techniques to aid users in understanding the key information within large collections of documents. This study explores the potential of data mining and clustering methods in extracting salient features from textual data and producing high-quality summaries. By applying K-means clustering and bisecting K-means clustering algorithms to the preprocessed textual data, the proposed approach groups similar sentences together and selects the most representative sentences from each cluster to form the final summary. The performance of the proposed method is evaluated using standard evaluation metrics, such as precision, recall, and F1-score, and compared with existing summarization techniques. The results demonstrate that the combination of data mining and clustering techniques provides a promising solution for generating accurate and concise document summaries, with potential applications in various domains, such as news aggregation, scientific literature summarization, and social media content analysis.   

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Sun, D., H. Fei, and Q. Li. "A Bisecting K-Medoids clustering Algorithm Based on Cloud Model." IFAC-PapersOnLine 51, no. 11 (2018): 308–15. http://dx.doi.org/10.1016/j.ifacol.2018.08.301.

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Santoso, Agus, and Siti Nurhayati. "ALGORITHMIC GUARANTEES FOR HIERARCHICAL DATA GROUPING: INSIGHTS FROM AVERAGE LINKAGE, BISECTING K-MEANS, AND LOCAL SEARCH HEURISTICS." International Journal of Intelligent Data and Machine Learning 2, no. 02 (2025): 8–13. https://doi.org/10.55640/ijidml-v02i02-02.

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Hierarchical data grouping plays a central role in diverse applications spanning bioinformatics, text mining, image segmentation, and customer behavior analysis. While a multitude of clustering algorithms have been proposed, including agglomerative techniques, divisive strategies, and heuristic optimizations, understanding their algorithmic guarantees and comparative performance remains an ongoing research challenge. This study provides a rigorous examination of the theoretical and empirical properties of three prominent approaches: average linkage clustering, bisecting k-means, and local search heuristics. We analyze their approximation bounds, convergence behaviors, and computational complexities under various objective functions, with particular emphasis on minimizing within-cluster variance and optimizing inter-cluster separation. Through formal proofs and experimental evaluation on benchmark datasets, we demonstrate that average linkage exhibits robust consistency and deterministic outcomes, though at the cost of higher computational overhead. In contrast, bisecting k-means provides scalable performance and favorable partitioning quality in high-dimensional settings, benefiting from recursive binary splitting. Local search heuristics offer flexible trade-offs between accuracy and efficiency, leveraging iterative refinement to escape suboptimal configurations. The findings underscore the importance of algorithm selection tailored to data characteristics and clustering objectives. This work contributes to a deeper understanding of the algorithmic guarantees associated with hierarchical data grouping and offers practical guidance for researchers and practitioners seeking principled, reliable clustering solutions.

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Wang, Yuyan, and Benjamin Moseley. "An Objective for Hierarchical Clustering in Euclidean Space and Its Connection to Bisecting K-means." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 6307–14. http://dx.doi.org/10.1609/aaai.v34i04.6099.

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This paper explores hierarchical clustering in the case where pairs of points have dissimilarity scores (e.g. distances) as a part of the input. The recently introduced objective for points with dissimilarity scores results in every tree being a ½ approximation if the distances form a metric. This shows the objective does not make a significant distinction between a good and poor hierarchical clustering in metric spaces.Motivated by this, the paper develops a new global objective for hierarchical clustering in Euclidean space. The objective captures the criterion that has motivated the use of divisive clustering algorithms: that when a split happens, points in the same cluster should be more similar than points in different clusters. Moreover, this objective gives reasonable results on ground-truth inputs for hierarchical clustering.The paper builds a theoretical connection between this objective and the bisecting k-means algorithm. This paper proves that the optimal 2-means solution results in a constant approximation for the objective. This is the first paper to show the bisecting k-means algorithm optimizes a natural global objective over the entire tree.

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Bilge, Alper, and Huseyin Polat. "A scalable privacy-preserving recommendation scheme via bisecting k-means clustering." Information Processing & Management 49, no. 4 (2013): 912–27. http://dx.doi.org/10.1016/j.ipm.2013.02.004.

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Cusick, T. W., and Yuan Li. "k-th order symmetric SAC boolean functions and bisecting binomial coefficients." Discrete Applied Mathematics 149, no. 1-3 (2005): 73–86. http://dx.doi.org/10.1016/j.dam.2005.02.006.

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Tania, Adela, Teny Handhayani, and Janson Hendryli. "PERBANDINGAN ANTARA ALGORITMA K-MEANS DAN ALGORITMA BISECTING K-MEANS DALAM MENGANALISIS GEMPA BUMI DI INDONESIA." Simtek : jurnal sistem informasi dan teknik komputer 8, no. 2 (2023): 265–70. http://dx.doi.org/10.51876/simtek.v8i2.205.

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Gempa bumi, khusus nya gempa tektonik adalah gempa yang paling sering terjadi di Indonesia. Hal itu dikarenakan kondisi geografis Indonesia yang terletak pada daerah pertemuan 3 batas lempeng tektonik dunia. Kondisi tersebut mendorong berbagai pihak dalam berupaya untuk siaga saat muncul potensi yang dapat ditimbulkan. Salah satu upaya yang dilakukan adalah dengan mengelompokkan wilayah kejadian gempa bumi di Indonesia yang memiliki potensi akan rawan terjadinya gempa bumi berdasarkan kedalaman dan kekuatan gempa bumi dengan menggunakan metode clustering. Metode clustering yang digunakan adalah Algoritma K-Means. Tujuan dari penelitian ini adalah untuk menganalisa pola spasial dari persebaran gempa di Indonesia. Data yang digunakan adalah data titik gempa di seluruh daerah di Indonesia dari November 2008 hingga Juni 2022 yang dicatat oleh Badan Meteorologi Klimatologi dan Geofisika (BMKG). Hasil clustering dengan menggunakan Algoritma K-Means menghasilkan 3 cluster dengan nilai rata-rata Silhouette Coefficient yaitu 0.7390 dan Davies Bouldin Index yaitu 0.4475. Selain itu dari penelitian ini juga didapatkan bahwa Algoritma K-Means memiliki nilai rata-rata Silhouette Coefficient dan Davies Bouldin Index lebih baik dibandingan dengan Algoritma Bisecting K-Means.

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Fei, Hongying, Nadine Meskens, and Claire-Hélène Moreau. "Clustering of patient trajectories with an auto-stopped bisecting K-Medoids algorithm." IFAC Proceedings Volumes 42, no. 4 (2009): 355–60. http://dx.doi.org/10.3182/20090603-3-ru-2001.0281.

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Janani, R. "TEXT DOCUMENT CLUSTERING USING ARTIFICIAL BEE COLONY WITH BISECTING K-MEANS ALGORITHM." International Journal of Advanced Research in Computer Science 9, no. 1 (2018): 619–23. http://dx.doi.org/10.26483/ijarcs.v9i1.5359.

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Fei, Hongying, and Nadine Meskens. "Clustering of Patients’ Trajectories with an Auto-Stopped Bisecting K-Medoids Algorithm." Journal of Mathematical Modelling and Algorithms in Operations Research 12, no. 2 (2012): 135–54. http://dx.doi.org/10.1007/s10852-012-9198-0.

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Ms., H. N. Gangavane. "A Comparison of ABK Means Algorithm with Traditional Algorithms." International Journal of Trend in Scientific Research and Development 1, no. 4 (2017): 614–21. https://doi.org/10.31142/ijtsrd2197.

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Crime investigation has very difficult task for police.Department of police plays an important role for identifying the criminals and their related information. It is observable that there are so manyamounts of increases in the crime rate due to the gap between the limitedusagesof investigation technologies. So, there are various new opportunities for the developing a new methodologies and techniques in this field for crime investigation. Using the methods like image processing, based on data mining, forensic, and social mining. Developing a good crime analysis tool to identify crime patterns quickly and efficiently for future crime pattern detection is required. Data mining is a process that uses a variety of data analysis tools to discover patterns and relationships in data that may be used to make valid predictions. Data mining techniques are the result of a long process of research and product development. Data mining is the computer assisted process to break up through and analyzing large amount of data. Then extracting the meaningfuldata. The proposed terminology provides combine approach of preprocessing by NLP clustering, outlier detection and rule engine to identify the criminals. To automatically group the retrieved data into a list of meaningful categories different clustering techniques can be used here we used the new approach to clustering i.e combination of K medoid and Bisecting K means algorithm for clustering. Crime area somewhat helps to find out the criminals so in this work we focus on area wise analysis with require records. Those records having all information about criminals which helps to further investigation. In this paper we compare ABK means algorithm with three basic clustering algorithms i.e. K means K medoid, and Bisecting K means on crime Denver dataset on the basis of time and accuracy. Ms. H. N. Gangavane "A Comparison of ABK-Means Algorithm with Traditional Algorithms" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-4 , June 2017, URL: https://www.ijtsrd.com/papers/ijtsrd2197.pdf

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Mor, Jyoti, Naresh Kumar, and Dinesh Rai. "EFFECTIVE PRESENTATION OF RESULTS USING RANKING & CLUSTERING IN META SEARCH ENGINE." COMPUSOFT: An International Journal of Advanced Computer Technology 07, no. 12 (2018): 2957–61. https://doi.org/10.5281/zenodo.14811030.

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The web is changing momentarily which makes it very difficult for the user to retrieve relevant results as per the given query. Clustering is a technique to organize search results in a way so that same search results are associated only with one cluster. For clustering of web pages, different parts of the webpage can be used. There are the lot of algorithms like K-means, Apriori, Expectation maximization, Ada etc. are used for clustering of documents. Clustering Algorithm such as K-means suffers from various problems such as less efficiency and clusters with large entropy. This paper overcomes the problems of K means and makes the use of bisecting K-means algorithm as the primary clustering algorithm having linear time. 

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Et.al, Syopiansyah Jaya Putra. "Optimizing Text Categorization for Indonesian Text Using Clustering Label Technique." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 3 (2021): 1483–91. http://dx.doi.org/10.17762/turcomat.v12i3.947.

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Text Categorization plays an important role for clustering the rapidly growing, yet unstructured, Indonesian text in digital format. Furthermore, it is deemed even more important since access to digital format text has become more necessary and widespread. There are many clustering algorithms used for text categorization. Unfortunately, clustering algorithms for text categorization cannot easily cluster the texts due to imperfect process of stemming and stopword of Indonesian language. This paper presents an intelligent system that categorizes Indonesian text documents into meaningful cluster labels. Label Induction Grouping Algorithm (LINGO) and Bisecting K- means are applied to process it through five phases, namely the pre-processing, frequent phrase extraction, cluster label induction, content discovery and final cluster formation. The experimental result showed that the system could categorize Indonesian text and reach to 93%. Furthermore, clustering quality evaluation indicates that text categorization using LINGO has high Precision and Recall with a value of 0.85 and 1, respectively, compare to Bisecting K-means which has a value of 0.78 and 0.99. Therefore, the result shows that LINGO is suitable for categorizing Indonesian text. The main contribution of this study is to optimize the clustering results by applying and maximizing text processing using Indonesian stemmer and stopword.

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Apraj, Saurabh D. "A Review on Artificial Intelligence in Stock Market." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (2022): 4358–60. http://dx.doi.org/10.22214/ijraset.2022.44946.

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Abstract: This paper essentially concentrates on the utilization of man-made consciousness and AI in the field of corporate share. The standards and qualities of KNN, k-Means, bisecting k-Means, and ANN algorithm are contemplated to analyse the impacts, similitudes and contrasts of various calculations. The calculations are carried out through Python programs for stock examination. As per the P/E proportion, profit rate, fixed resource turnover rate, net revenue and different marks of each stock, the stocks are characterized and grouped to anticipate the stock improvement prospects and give reference to choosing fitting speculation systems.

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Savaresi, Sergio M., and Daniel L. Boley. "A comparative analysis on the bisecting K-means and the PDDP clustering algorithms." Intelligent Data Analysis 8, no. 4 (2004): 345–62. http://dx.doi.org/10.3233/ida-2004-8403.

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Das, Prabhat, Karthik Kovuri, and Sajal Saha. "SCALABILITY AND EFFICIENCY OF CLUSTERING ALGORITHMS FOR LARGE-SCALE IoT DATA: A COMPARATIVE ANALYSIS." Journal of Theoretical and Applied Information Technology 103, no. 10 (2025): 4227–44. https://doi.org/10.5281/zenodo.15565482.

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This research investigates the scalability and efficiency of clustering algorithms applied to large-scale Internet of Things (IoT) data. A comprehensive evaluation is conducted on fourteen clustering algorithms—Affinity Propagation, Agglomerative, BIRCH, Bisecting K-Means, DBSCAN, Fuzzy C-Means, Gaussian Mixtures, HDBSCAN, K-Means, Mean-Shift, OPTICS, Overlapping K-Means, Spectral Clustering, and Ward- Hierarchical—across datasets ranging from 40,000 to 100,000 sensor readings. The study systematically analyzes execution time and clustering performance to determine their suitability for large-scale IoT applications. Results indicate that K-Means, Ward-Hierarchical, and BIRCH exhibit strong scalability and computational efficiency, whereas Affinity Propagation and Spectral Clustering face significant challenges with increasing dataset size. These findings provide valuable guidance for selecting optimal clustering techniques in IoT-based data analytics, considering factors such as computational constraints, dataset characteristics, and clustering granularity.

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A.Ananda, Shankar, and Kumar Dr.K.R.Ananda. "Data Mining Technique for Opinion Retrieval in Healthcare System." International Journal of Data Mining & Knowledge Management Process (IJDKP) 5, no. 5 (2019): 75–84. https://doi.org/10.5281/zenodo.3463239.

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The aim of this paper is to use Text mining(TM) concepts in the field of Health care System. We no that now days decision making in health care involves number of opinions given by the group of medical experts for specific disease in the form of decisions which will be presented in medical database in the form of text. These decisions are then mined from database with the help of Data Mining techniques. Text document clustering is considered as tool for performing information based operations. For clustering normally K-means clustering technique is used. In this paper we use Bisecting K-means clustering technique and it is better compared to traditional K-means technique. The objective is to study the revealed groupings of similar opinion-types associated with the likelihood of physicians and medical experts.

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Zhao, Chunhui, Xiaocui Li, and Yan Cang. "Bisecting k-means clustering based face recognition using block-based bag of words model." Optik - International Journal for Light and Electron Optics 126, no. 19 (2015): 1761–66. http://dx.doi.org/10.1016/j.ijleo.2015.04.068.

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Kovaleva, Ekaterina V., and Boris G. Mirkin. "Bisecting K-Means and 1D Projection Divisive Clustering: A Unified Framework and Experimental Comparison." Journal of Classification 32, no. 3 (2015): 414–42. http://dx.doi.org/10.1007/s00357-015-9186-y.

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Zhang, Fuzhi, and Shilei Wang. "Detecting Group Shilling Attacks in Online Recommender Systems Based on Bisecting K-Means Clustering." IEEE Transactions on Computational Social Systems 7, no. 5 (2020): 1189–99. http://dx.doi.org/10.1109/tcss.2020.3013878.

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Vinicius de Andrade Lima, Marcos, Thales Mesquita Sousa, and João Batista Carvalho Nunes. "Aplicando Mineração de Dados Educacionais para a Redistribuição dos Distritos de Educação de Fortaleza." RENOTE 18, no. 2 (2021): 346–57. http://dx.doi.org/10.22456/1679-1916.110253.

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É importante que órgãos governamentais no setor educacional tenham embasamento de estudos que envolvam políticas públicas educacionais para a tomada de decisão. A cidade de Fortaleza abriga a quarta maior rede municipal de ensino do País e possui apenas seis Distritos de Educação para dar suporte às escolas. Esta pesquisa busca, por meio da Mineração de Dados Educacionais, mostrar o número e a localização geográfica ideais dos Distritos de Educação, de modo que eles melhor atendam o parque escolar instalado na cidade. Recorreu-se, então, a algoritmos de agrupamento não supervisionado (K-Means, Bisecting K-Means e Gaussian Mixture Model). Os achados da pesquisa estão em sintonia com a nova divisão de Fortaleza em 12 regiões e auxiliam no planejamento de futura redistribuição dos Distritos.

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Vargas, Victória, Eduardo Rodrigues Amorim, José André de Moura Brito, and Gustavo Silva Semaan. "Variantes do Índice Silhueta para Validação de Agrupamentos." Cadernos do IME - Série Informática 46 (June 28, 2022): 118–27. http://dx.doi.org/10.12957/cadinf.2021.68558.

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O presente artigo traz a proposta de avaliação de quatro variantes do índice de silhueta quanto `a sua capacidade de detectar soluções de boa qualidade para problemas de agrupamento. Neste sentido, foram realizados cinco experimentos computacionais, contemplando 51 instâncias da literatura diversificadas (dados reais e artificiais). Como medidas de dissimilaridade foram utilizadas as distâncias euclidiana e de Manhattan, além de três algoritmos clássicos de agrupamento, a saber: PAM, DBSCAN e Bisecting k-means. De modo adicional, experimentos com a Estatística de Hopkins foram realizados com o intuito de verificar a existência de tendência de agrupamentos nas instâncias reais, em que o número de grupos k não é conhecido a priori. Os resultados obtidos indicam que a variante baseada na mediana constitui-se como boa alternativa para detectar soluções de qualidade.

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Wang, Jie, Chengzhi Zhang, Mengying Zhang, and Sanhong Deng. "CitationAS: A Tool of Automatic Survey Generation Based on Citation Content." Journal of Data and Information Science 3, no. 2 (2018): 20–37. http://dx.doi.org/10.2478/jdis-2018-0007.

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Abstract Purpose This study aims to build an automatic survey generation tool, named CitationAS, based on citation content as represented by the set of citing sentences in the original articles. Design/methodology/approach Firstly, we apply LDA to analyse topic distribution of citation content. Secondly, in CitationAS, we use bisecting K-means, Lingo and STC to cluster retrieved citation content. Then Word2Vec, WordNet and combination of them are applied to generate cluster labels. Next, we employ TF-IDF, MMR, as well as considering sentence location information, to extract important sentences, which are used to generate surveys. Finally, we adopt manual evaluation for the generated surveys. Findings In experiments, we choose 20 high-frequency phrases as search terms. Results show that Lingo-Word2Vec, STC-WordNet and bisecting K-means-Word2Vec have better clustering effects. In 5 points evaluation system, survey quality scores obtained by designing methods are close to 3, indicating surveys are within acceptable limits. When considering sentence location information, survey quality will be improved. Combination of Lingo, Word2Vec, TF-IDF or MMR can acquire higher survey quality. Research limitations The manual evaluation method may have a certain subjectivity. We use a simple linear function to combine Word2Vec and WordNet that may not bring out their strengths. The generated surveys may not contain some newly created knowledge of some articles which may concentrate on sentences with no citing. Practical implications CitationAS tool can automatically generate a comprehensive, detailed and accurate survey according to user’s search terms. It can also help researchers learn about research status in a certain field. Originality/value CitaitonAS tool is of practicability. It merges cluster labels from semantic level to improve clustering results. The tool also considers sentence location information when calculating sentence score by TF-IDF and MMR.

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K., Aparna, and Mydhili K. Nair. "Development of Fractional Genetic PSO Algorithm for Multi Objective Data Clustering." International Journal of Applied Evolutionary Computation 7, no. 3 (2016): 1–16. http://dx.doi.org/10.4018/ijaec.2016070101.

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Clustering is the task of finding natural partitioning within a data set such that data items within the same group are more similar than those within different groups. The performance of the traditional K-Means and Bisecting K-Means algorithm degrades as the dimensionality of the data increases. In order to find better clustering results, it is important to enhance the traditional algorithms by incorporating various constraints. Hence it is planned to develop a Multi-Objective Optimization (MOO) technique by including different objectives, like MSE, Stability measure, DB index, XB-index and sym-index. These five objectives will be used as fitness function for the proposed Fractional Genetic PSO algorithm (FGPSO) which is the hybrid optimization algorithm to do the clustering process. The performance of the proposed multi objective FGPSO algorithm will be evaluated based on clustering accuracy. Finally, the applicability of the proposed algorithm will be checked for some benchmark data sets available in the UCI machine learning repository.

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Apriliawan, Yohanes Eki. "Pola Spasial Aksesibilitas Fasilitas Publik Kota Pekalongan: Pendekatan Grid dan Machine Learning." JURNAL LITBANG KOTA PEKALONGAN 22, no. 2 (2024): 139–53. https://doi.org/10.54911/litbang.v22i2.959.

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Penelitian ini menganalisis pola aksesibilitas infrastruktur di Kota Pekalongan menggunakan pendekatan berbasis grid dan metode pembelajaran mesin. Dengan mengintegrasikan data dari BPS, OpenStreetMap, dan ESRI 2023, penelitian ini menggunakan unit analisis grid 100m × 100m untuk mengukur aksesibilitas ke fasilitas publik seperti pendidikan, kesehatan, dan perdagangan. Analisis menggunakan tiga metode pengelompokan (K-Means, Bisecting K-Means, dan Agglomerative) mengidentifikasi tiga pola aksesibilitas yang khas. Klaster pertama (40,29%) menunjukkan aksesibilitas optimal dengan kepadatan jalan yang tinggi, terutama di pusat kota. Klaster kedua (31,64%) menunjukkan aksesibilitas sedang, mencirikan daerah transisi. Klaster ketiga (32,90%) menunjukkan aksesibilitas terendah, terutama di wilayah selatan dan pesisir. Pemodelan pembelajaran mesin menggunakan Catboost mencapai akurasi tertinggi dengan nilai logloss 0,0091, yang mengonfirmasi jarak ke fasilitas kesehatan dan komersial sebagai penentu utama aksesibilitas. Temuan ini memberikan landasan empiris untuk pengembangan infrastruktur yang lebih terarah, dengan rekomendasi kebijakan yang disesuaikan dengan karakteristik masing-masing klaster. Metodologi yang dikembangkan menawarkan pendekatan baru untuk analisis aksesibilitas perkotaan yang dapat direplikasi di kota-kota lain dengan karakteristik serupa.

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Rodríguez-Esparragón, Dionisio, Paolo Gamba, and Javier Marcello. "Automatic Methodology for Forest Fire Mapping with SuperDove Imagery." Sensors 24, no. 16 (2024): 5084. http://dx.doi.org/10.3390/s24165084.

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The global increase in wildfires due to climate change highlights the need for accurate wildfire mapping. This study performs a proof of concept on the usefulness of SuperDove imagery for wildfire mapping. To address this topic, we present an automatic methodology that combines the use of various vegetation indices with clustering algorithms (bisecting k-means and k-means) to analyze images before and after fires, with the aim of improving the precision of the burned area and severity assessments. The results demonstrate the potential of using this PlanetScope sensor, showing that the methodology effectively delineates burned areas and classifies them by severity level, in comparison with data from the Copernicus Emergency Management Service (CEMS). Thus, the potential of the SuperDove satellite sensor constellation for fire monitoring is highlighted, despite its limitations regarding radiometric distortion and the absence of Short-Wave Infrared (SWIR) bands, suggesting that the methodology could contribute to better fire management strategies.

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Liu, Pengfei, Dong Xu, Jingguo Li, et al. "Damage mode identification of composite wind turbine blade under accelerated fatigue loads using acoustic emission and machine learning." Structural Health Monitoring 19, no. 4 (2019): 1092–103. http://dx.doi.org/10.1177/1475921719878259.

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This article studies experimentally the damage behaviors of a 59.5-m-long composite wind turbine blade under accelerated fatigue loads using acoustic emission technique. First, the spectral analysis using the fast Fourier transform is used to study the components of acoustic emission signals. Then, three important objectives including the attenuation behaviors of acoustic emission waves, the arrangement of sensors as well as the detection and positioning of defect sources in the composite blade by developing the time-difference method among different acoustic emission sensors are successfully reached. Furthermore, the clustering analysis using the bisecting K-means method is performed to identify different damage modes for acoustic emission signal sources. This work provides a theoretical and technique support for safety precaution and maintaining of in-service blades.

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Jayabharathy, J., and S. Kanmani. "Correlation-based concept-oriented bisecting k-means clustering and topic detection for scientific literature and news tracks." International Journal of Knowledge Engineering and Data Mining 3, no. 2 (2015): 170. http://dx.doi.org/10.1504/ijkedm.2015.071285.

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Lee, Jiwon, Jeongheun Kang, Chun-Su Park, and Jongpil Jeong. "Distributed Fire Classification and Localization Model Based on Federated Learning with Image Clustering." Applied Sciences 14, no. 20 (2024): 9162. http://dx.doi.org/10.3390/app14209162.

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In this study, we propose a fire classification system using image clustering based on a federated learning (FL) structure. This system enables fire detection in various industries, including manufacturing. The accurate classification of fire, smoke, and normal conditions is an important element of fire prevention and response systems in industrial sites. The server in the proposed system extracts data features using a pretrained vision transformer model and clusters the data using the bisecting K-means algorithm to obtain weights. The clients utilize these weights to cluster local data with the K-means algorithm and measure the difference in data distribution using the Kullback–Leibler divergence. Experimental results show that the proposed model achieves nearly 99% accuracy on the server, and the clustering accuracy on the clients remains high. In addition, the normalized mutual information value remains above 0.6 and the silhouette score reaches 0.9 as the rounds progress, indicating improved clustering quality. This study shows that the accuracy of fire classification is enhanced by using FL and clustering techniques and has a high potential for real-time detection.

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Meng, Zu Qiang, Shi Mo Shen, and Qiu Lian Chen. "A Network Decomposition-Based Text Clustering Algorithm for Topic Detection." Applied Mechanics and Materials 239-240 (December 2012): 1318–23. http://dx.doi.org/10.4028/www.scientific.net/amm.239-240.1318.

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Text clustering is one of the most popular topic detection techniques. However, the existing text clustering approaches require that each document has to be partitioned to one and only one cluster. This is not reasonable in some cases for there exist some documents which should not used to constitute topics. This paper firstly models a text document set as a network and designs a method for decomposing such a network, and then proposes a truly original text clustering algorithm for topic detection, called a network decomposition-based text clustering algorithm for topic detection (NDTCATD). The proposed algorithm ensures that meaningless documents can not be used to constitute topics. Experimental results show that NDTCATD is much better than bisecting k-means algorithm in terms of overall similarity and average cluster similarity. Therefore the proposed algorithm is reasonable and effective and is especially suitable for topic detection.

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Correia, Felipe Pinheiro, Samara Ruthielle da Silva, Fabricio Braga Soares de Carvalho, Marcelo Sampaio de Alencar, Karcius Day Rosario Assis, and Rodrigo Moreira Bacurau. "LoRaWAN Gateway Placement in Smart Agriculture: An Analysis of Clustering Algorithms and Performance Metrics." Energies 16, no. 5 (2023): 2356. http://dx.doi.org/10.3390/en16052356.

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The use of Wireless Sensor Networks (WSN) in smart agriculture has emerged in recent years. LoRaWAN (Long Range Wide Area Networks) is widely recognized as one of the most suitable technologies for this application, due to its capacity to transmit data over long distances while consuming little energy. Determining the number and location of gateways (GWs) in a production setting is one of the most challenging tasks of planning and building this type of network. Various solutions to the LoRaWAN gateway placement problem have been proposed in the literature, utilizing clustering algorithms; however, few works have compared the performance of various strategies. Considering all these facts, this paper proposes a strategy for planning the number and localization of LoRaWAN GWs, to cover a vast agricultural region. Four clustering algorithms were used to deploy the network GWs: K-Means and its three versions: Minibatch K-Means; Bisecting K-Means; and Fuzzy c-Means (FCM). As performance metrics, uplink delivery rate (ULDR) and energy consumption were used, to provide subsidies for the network designer and the client, with which to choose the best setup. A stochastic energy model was used to evaluate power consumption. Simulations were performed, considering two scenarios: Scenario 1 with lower-medium concurrence, and Scenario 2 with higher-medium concurrence. The simulations showed that the use of more than two GWs in Scenario 1 did not lead to significant improvements in ULDR and energy consumption, whereas, in Scenario 2, the suggested number of GWs was between 11 and 15. The results showed that for Scenario 1, the FCM algorithm was superior to all alternatives, regarding the ULDR and mean energy consumption, while the K-Means algorithm was superior with respect to maximum energy consumption. In relation to Scenario 2, K-Means caused the best ULDR and mean consumption, while FCM produced the lowest maximum consumption.

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Zhang, Siyu. "Research on intelligent information system of user intelligent behavior data based on computer big data." Highlights in Science, Engineering and Technology 9 (September 30, 2022): 206–11. http://dx.doi.org/10.54097/hset.v9i.1777.

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This paper collects and analyzes users' online behaviors through computer big data technology, uses computer big data intelligent analysis system to analyze users' online shopping behavior, and uses information-based data analysis system to detect consumers' online shopping needs under the e-commerce platform. The main technique used in this paper is the computer browser log mining method. In the user's click stream data, the function keys of Tmall and Taobao webpages are used as data information for classification and collection. This paper uses the Bisecting K-means clustering algorithm to mine the state of interest. Finally, the feature maps of interests and behaviors are summarized. By processing four typical types of e-commerce user demand status, including background management type, continuous search type, product browsing type and information search data, and clustering based on page type, an effective method for dynamically changing demand judgment is obtained. The state of online shoppers is analyzed through data processing, which also proves the effectiveness of the computer intelligent information system.

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Zhang, Yu-Hui, and Zi-Jia Wang. "Peak Identification in Evolutionary Multimodal Optimization: Model, Algorithms, and Metrics." Biomimetics 9, no. 10 (2024): 643. http://dx.doi.org/10.3390/biomimetics9100643.

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In this paper, we present a two-phase multimodal optimization model designed to efficiently and accurately identify multiple optima. The first phase employs a population-based search algorithm to locate potential optima, while the second phase introduces a novel peak identification (PI) procedure to filter out non-optimal solutions, ensuring that each identified solution represents a distinct optimum. This approach not only enhances the effectiveness of multimodal optimization but also addresses the issue of redundant solutions prevalent in existing algorithms. We propose two PI algorithms: HVPI, which uses a hill–valley approach to distinguish between optima, without requiring prior knowledge of niche radii; and HVPIC, which integrates HVPI with bisecting K-means clustering to reduce the number of fitness evaluations (FEs). The performance of these algorithms was evaluated using the F-measure, a comprehensive metric that accounts for both the accuracy and redundancy in the solution set. Extensive experiments on a suite of benchmark functions and engineering problems demonstrated that our proposed algorithms achieved a high precision and recall, significantly outperforming traditional methods.

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Dhanashri, Ingale*, and M. M. Kshirsagar Dr. "A NOVEL APPROACH TO INFER USER SEARCH GOALS FOR OPTIMIZE RESULT." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 4 (2016): 576–82. https://doi.org/10.5281/zenodo.49797.

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Search engine is one of the most significant applications for internet users. Different users may have different search targets when they submit broad-topic to a search engine. Most times, search engine does not deliver what user needs. So to produce best relevant results, there is necessity to analyse user goals behind searching. Inference and analysis of it can be very useful in improving quality of a search engine's results. In this paper, a framework is projected to examine user search areas for a query by clustering the feedback sessions. Feedback sessions are made from user click-through logs and can more accurately predict the information requirements of users. Post feedback session formations, pseudo-documents are generated which better illustrate feedback sessions for clustering. For clustering, bisecting K-means algorithm is used. After cluster formations, restructuring of web search results is done by calculating smallest distance. At the end, to evaluate the performance of inferring user search goals, a new criterion “Classified Average Precision (CAP)” is proposed.

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Zhou, Zihao, Aihua Ran, Shuxiao Chen, et al. "A fast screening framework for second-life batteries based on an improved bisecting K-means algorithm combined with fast pulse test." Journal of Energy Storage 31 (October 2020): 101739. http://dx.doi.org/10.1016/j.est.2020.101739.

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LIN, ROBERT, REN-GUEY LEE, CHWAN-LU TSENG, YAN-FA WU, and JOE-AIR JIANG. "DESIGN AND IMPLEMENTATION OF WIRELESS MULTI-CHANNEL EEG RECORDING SYSTEM AND STUDY OF EEG CLUSTERING METHOD." Biomedical Engineering: Applications, Basis and Communications 18, no. 06 (2006): 276–83. http://dx.doi.org/10.4015/s1016237206000427.

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A multi-channel wireless EEG (electroencephalogram) acquisition and recording system is developed in this work. The system includes an EEG sensing and transmission unit and a digital processing circuit. The former is composed of pre-amplifiers, filters, and gain amplifiers. The kernel of the later digital processing circuit is a micro-controller unit (MCU, TI-MSP430), which is utilized to convert the EEG signals into digital signals and fulfill the digital filtering. By means of Bluetooth communication module, the digitized signals are sent to the back-end such as PC or PDA. Thus, the patient's EEG signal can be observed and stored without any long cables such that the analogue distortion caused by long distance transmission can be reduced significantly. Furthermore, an integrated classification method, consisting of non-linear energy operator (NLEO), autoregressive (AR) model, and bisecting k-means algorithm, is also proposed to perform EEG off-line clustering at the back-end. First, the NLEO algorithm is utilized to divide the EEG signals into many small signal segments according to the features of the amplitude and frequency of EEG signals. The AR model is then applied to extract two characteristic values, i.e., frequency and amplitude (peak to peak value), of each segment and to form characteristic matrix for each segment of EEG signal. Finally, the improved modified k-means algorithm is utilized to assort similar EEG segments into better data classification, which allows accessing the long-term EEG signals more quickly.

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Nonnis, Marcello, Mirian Agus, Monica Piera Pirrone, Stefania Cuccu, Maria Luisa Pedditzi, and Claudio Giovanni Cortese. "Burnout and Engagement Dimensions in the Reception System of Illegal Immigration in the Mediterranean Sea. A Qualitative Study on a Sample of Italian Practitioners." International Journal of Environmental Research and Public Health 18, no. 7 (2021): 3726. http://dx.doi.org/10.3390/ijerph18073726.

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The present study describes the semantic nature of burnout and engagement in the operators involved in the management of illegal immigration. Semi-structured interviews were conducted on a sample of Italian practitioners (n = 62) of the two levels of the reception system considered: (1) rescue and first aid and (2) reception and integration. Within the framework of the job demands–resources model (JD-R), the interviews deepened the analysis of the positive and negative dimensions of burnout and engagement: exhaustion versus energy, relational deterioration versus relational involvement, professional inefficacy versus professional efficacy and disillusion versus trust. The interviews were analysed using the T-Lab software, through a cluster analysis (bisecting K-means algorithm), which emphasised noteworthy themes. The results show that, in the vast majority of the dimensions considered (for both levels of reception), the same dimensions of engagement of the operators (energy, relational involvement, professional efficacy and trust) are able to lead them into a condition of burnout, with experiences, conversely, of exhaustion, relational deterioration, professional inefficacy and disillusion. These findings expand the knowledge on burnout and engagement in practitioners of illegal immigration, a context characterised by the value of help and welcome.

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Khosravi Kazazi, Ali, Fariba Amiri, Yaser Rahmani, Raheleh Samouei, and Hamidreza Rabiei-Dastjerdi. "A New Hybrid Model for Mapping Spatial Accessibility to Healthcare Services Using Machine Learning Methods." Sustainability 14, no. 21 (2022): 14106. http://dx.doi.org/10.3390/su142114106.

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The unequal distribution of healthcare services is the main obstacle to achieving health equity and sustainable development goals. Spatial accessibility to healthcare services is an area of interest for health planners and policymakers. In this study, we focus on the spatial accessibility to four different types of healthcare services, including hospitals, pharmacies, clinics, and medical laboratories at Isfahan’s census blocks level, in a multivariate study. Regarding the nature of spatial accessibility, machine learning unsupervised clustering methods are utilized to analyze the spatial accessibility in the city. Initially, the study area was grouped into five clusters using three unsupervised clustering methods: K-Means, agglomerative, and bisecting K-Means. Then, the intersection of the results of the methods is considered to be conclusive evidence. Finally, using the conclusive evidence, a supervised clustering method, KNN, was applied to generate the map of the spatial accessibility situation in the study area. The findings of this study show that 47%, 22%, and 31% of city blocks in the study area have rich, medium, and poor spatial accessibility, respectively. Additionally, according to the study results, the healthcare services development is structured in a linear pattern along a historical avenue, Chaharbagh. Although the scope of this study was limited in terms of the supply and demand rates, this work gives more information and spatial insights for researchers, planners, and policymakers aiming to improve accessibility to healthcare and sustainable urban development. As a recommendation for further research work, it is suggested that other influencing factors, such as the demand and supply rates, should be integrated into the method.

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Jan, Malang, Shah Khusro, Iftikhar Alam, Inayat Khan, and Badam Niazi. "Interest-Based Content Clustering for Enhancing Searching and Recommendations on Smart TV." Wireless Communications and Mobile Computing 2022 (May 5, 2022): 1–14. http://dx.doi.org/10.1155/2022/3896840.

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Smart TV has become a pervasive device due to its support for numerous entertainment options. These capabilities of smart TV make it attractive for viewers and researcher. Besides, a plethora of multimedia content continues to grow, which makes searching and browsing the desired content a difficult, time-consuming, and contributes to cognitive overload problem. In the case of smart TV, making clusters of the related content based on user’s interest is among the best solutions. In this connection, this study proposed a dynamic approach for clustering the TV-related online multimedia content and presenting them in a manageable format on smart TV to mitigate the issue of searching and relevant recommendations. We collected and clustered the content from diverse data sources based on the viewer’s interest. This further recommends novel content to the viewers without social metadata, such as rates, tags, which is normally insignificant in for smart TV viewership due to its shared nature. We used bisecting K -means, Lingo, and Suffix Tree Clustering (STC) algorithms. A comparative analysis of these algorithms and suitability in the context of smart TV is also presented. Results show that the proposed approach enhances search results and recommends relevant content based on user’s interests.

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Liu, Yajing, Ruijie Cai, Xiaokang Yin, and Shengli Liu. "An Exploit Traffic Detection Method Based on Reverse Shell." Applied Sciences 13, no. 12 (2023): 7161. http://dx.doi.org/10.3390/app13127161.

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As the most crucial link in the network kill chain, exploiting a vulnerability is viewed as one of the most popular attack vectors to get the control authority of the system, which is dangerous for legal users. Therefore, an effective exploit traffic detection method is urgent. However, current methods are almost based on pattern matching, invalid for encrypted traffic. To address this problem, we propose a reverse shell-based exploit traffic detection method, ETDetector. Our key insight is that the reverse shell attack often coexists with vulnerability exploitation as one of the most popular exploit behaviors. So, we first extract the fusion information feature from original features, such as the packet delay sequence, as input of a decision tree model to identify reverse shell traffic in the shellcode execution stage. Then, we trace suspicious traffic in the shellcode delivery stage by reconstructing the session relationship of the two stages above. Compared with Blatta, using a cyclic neural network to detect early exploit traffic, the detection rate of ETDetector is increased by 50% and valid for encrypted exploit traffic. In addition, we propose a traffic stratification method based on a bisecting K-means algorithm, which can intuitively show the traffic communication behavior and improve the interpretability of ETDetector.

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Higashi, Mikito, Takeshi Yoshimura, Noriyoshi Usui, et al. "A Potential Serum N-glycan Biomarker for Hepatitis C Virus-Related Early-Stage Hepatocellular Carcinoma with Liver Cirrhosis." International Journal of Molecular Sciences 21, no. 23 (2020): 8913. http://dx.doi.org/10.3390/ijms21238913.

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Detection of early-stage hepatocellular carcinoma (HCC) is beneficial for prolonging patient survival. However, the serum markers currently used show limited ability to identify early-stage HCC. In this study, we explored human serum N-glycans as sensitive markers to diagnose HCC in patients with cirrhosis. Using a simplified fluorescence-labeled N-glycan preparation method, we examined non-sialylated and sialylated N-glycan profiles from 71 healthy controls and 111 patients with hepatitis and/or liver cirrhosis (LC) with or without HCC. We found that the level of serum N-glycan A2G1(6)FB, a biantennary N-glycan containing core fucose and bisecting GlcNAc residues, was significantly higher in hepatitis C virus (HCV)-infected cirrhotic patients with HCC than in those without HCC. In addition, A2G1(6)FB was detectable in HCV-infected patients with early-stage HCC and could be a more accurate marker than alpha-fetoprotein (AFP) or protein induced by vitamin K absence or antagonists-II (PIVKA-II). Moreover, there was no apparent correlation between the levels of A2G1(6)FB and those of AFP or PIVKA-II. Thus, simultaneous use of A2G1(6)FB and traditional biomarkers could improve the accuracy of HCC diagnosis in HCV-infected patients with LC, suggesting that A2G1(6)FB may be a reliable biomarker for early-stage HCC patients.

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Wen, Xiamei, Liping Fu, Ting Fu, Jessica Keung, and Ming Zhong. "Driver Behavior Classification at Stop-Controlled Intersections Using Video-Based Trajectory Data." Sustainability 13, no. 3 (2021): 1404. http://dx.doi.org/10.3390/su13031404.

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Understanding how drivers behave at stop-controlled intersection is of critical importance for the control and management of an urban traffic system. It is also a critical element of consideration in the burgeoning field of smart infrastructure and connected and autonomous vehicles (CAV). A number of past efforts have been devoted to investigating the driver behavioral patterns when they pass through stop-controlled intersections. However, the majority of these studies have been limited to qualitative descriptions and analyses of driver behavior due to the unavailability of high-resolution vehicle data and sound methodology for classifying various driver behaviors. In this paper, we introduce a methodology that uses computer-vision vehicle trajectory data and unsupervised clustering techniques to classify different types of driver behaviors, infer the underlying mechanism and compare their impacts on safety. Two major types of behaviors are investigated, including vehicle stopping behavior and vehicle approaching patterns, using two clustering algorithms: a bisecting K-means algorithm for classifying stopping behavior, and the improved density-based spatial clustering of applications with noise (DBSCAN) algorithm for classifying vehicle approaching patterns. The methodology is demonstrated using a case study involving five stop-controlled intersections in Montreal, Canada. The results from the analysis show that there exist five distinctive classes of driver behaviors representing different levels of risk in both vehicle stopping and approaching processes. This finding suggests that the proposed methodology could be applied to develop new safety surrogate measures and risk analysis methods for network screening and countermeasure analyses of stop-controlled intersections.

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