Optimizing Latent Space Representation for Tourism Insights: A Metaheuristic Approach
Abstract
In the modern digital era, social media platforms with travel reviews significantly influence the tourism industry by providing a wealth of information on consumer preferences and behaviors. However, these textual reviews' complex and varied nature poses analytical challenges. This research employs advanced Natural Language Processing (NLP) techniques to process and analyze vast amounts of travel data efficiently, tackling the challenges posed by the diverse and detailed content in the tourism field. We have developed an innovative text clustering methodology that combines BERT's deep linguistic analysis capabilities (Bidirectional Encoder Representations from Transformers) with the thematic organization strengths of LDA (Latent Dirichlet Allocation). This hybrid model, further refined with the dimensionality reduction capabilities of ELM-AE and the optimization precision of PPSO (Phasor Particle Swarm Optimization), yields concise, contextually enriched text representations. Such refined data representations enhance the accuracy of K-means clustering, facilitating nuanced topic identification within the complex domain of travel reviews. This approach streamlines feature extraction and ensures rapid training and minimal loss, underscoring the model's effectiveness in distilling and reconstructing textual features. Our application of this hybrid LDA-BERT model to analyze TripAdvisor reviews of Thailand's shopping destinations reveals meaningful insights, significantly aiding in understanding customer experiences. Despite its contributions, this study acknowledges limitations, including biases in user-generated content and the intricacies of accurately interpreting sentiments and contexts within reviews. This research marks a significant step forward in utilizing NLP for tourism industry analysis, providing a pathway for future investigations to build upon.
Keywords
Full Text:
PDFReferences
Z. Xiang and U. Gretzel, "Role of social media in Online Travel Information Search," Tourism Management, vol. 31, no. 2, pp. 179–188, 2010, doi: 10.1016/j.tourman.2009.02.016.
S. Pike, L. P. Dam, and A. Beatson, "Social media gratifications in the context of international travel planning: The use of the repertory test method," Acta turistica, vol. 31, no. 2, pp. 153–178, 2019, doi: 10.22598/at/2019.31.2.153.
R. A. Hamid et al., “How smart is e-tourism? A systematic review of Smart Tourism Recommendation System Applying Data Management," Computer Science Review, vol. 39, p. 100337, 2021, doi: 10.1016/j.cosrev.2020.100337.
M. F. ALmasoodi, S. Rahman, M. Basendwah, and A. N. ALfarra, "Leveraging Digital Transformation to enhance quality tourism services in Babylon City, Iraq," International Journal of Sustainable Development and Planning, vol. 18, no. 10, pp. 3195–3211, 2023, doi: 10.18280/ijsdp.181020.
J. Žižka, F. Dařena, and A. Svoboda, “Introduction to text mining with Machine Learning,” Text Mining with Machine Learning, pp. 1–12, Oct. 2019, doi: 10.1201/9780429469275-1.
Q. Li et al., “A survey on text classification: From traditional to deep learning,” ACM Transactions on Intelligent Systems and Technology, vol. 13, no. 2, pp. 1–41, Apr. 2022, doi: 10.1145/3495162.
Q. He, K. Chang, E.-P. Lim, and J. Zhang, "Bursty feature representation for clustering text streams," Proceedings of the 2007 SIAM International Conference on Data Mining, pp. 491-496, 2007, doi: 10.1137/1.9781611972771.50.
E. C. Garrido-Merchan, R. Gozalo-Brizuela, and S. Gonzalez-Carvajal, “Comparing Bert against traditional machine learning models in text classification,” Journal of Computational and Cognitive Engineering, vol. 2, no. 4, pp. 352-356, Apr. 2023, doi: 10.47852/bonviewjcce3202838.
A. S. Alammary, “Bert models for Arabic Text Classification: A systematic review,” Applied Sciences, vol. 12, no. 11, p. 5720, Jun. 2022, doi: 10.3390/app12115720.
M. Mishra and J. Viradiya, "Survey of Sentence Embedding Methods," International Journal of Applied Science and Computations, vol. 6, no. 3, pp. 592-592, 2019, doi: 10.13140/RG.2.2.21861.45289.
D. M. Blei, A. Y. Ng, and M. I. Jordan, "Latent dirichlet allocation," Journal of machine Learning research, vol. 3, pp. 993-1022, Jan 2003.
M. A. Ferraria, V. A. Ferraria, and L. N. de Castro, "An investigation into different text representations to train an artificial immune network for clustering texts," International Journal of Interactive Multimedia and Artificial Intelligence, vol. 8, no. 3, p. 55, 2023, doi: 10.9781/ijimai.2023.08.006.
Y. Tay, M. Dehghani, D. Bahri, and D. Metzler, “Efficient transformers: A survey,” ACM Computing Surveys, vol. 55, no. 6, pp. 1–28, Dec. 2022, doi: 10.1145/3530811.
M. H. Ahmed, S. Tiun, N. Omar, and N. S. Sani, "Short text clustering algorithms, application and challenges: A survey," Applied Sciences, vol. 13, no. 1, p. 342, 2022, doi: 10.3390/app13010342.
E. Keogh and A. Mueen, "Curse of dimensionality," Encyclopedia of Machine Learning and Data Mining, pp. 314–315, 2017, doi: 10.1007/978-1-4899-7687-1_192.
R. Wang, J. Bian, F. Nie, and X. Li, "Unsupervised Discriminative Projection for Feature Selection," in IEEE Transactions on Knowledge and Data Engineering, vol. 34, no. 2, pp. 942-953, 1 Feb. 2022, doi: 10.1109/TKDE.2020.2983396.
V. Raunak, V. Gupta, and F. Metze, “Effective dimensionality reduction for word embeddings,” Proceedings of the 4th Workshop on Representation Learning for NLP, pp. 235-243, 2019, doi: 10.18653/v1/w19-4328.
I. T. Jolliffe and J. Cadima, “Principal component analysis: A review and recent developments,” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 374, no. 2065, p. 20150202, Apr. 2016, doi: 10.1098/rsta.2015.0202.
K. Pearson, “LIII. on lines and planes of closest fit to systems of points in space,” The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, vol. 2, no. 11, pp. 559–572, Nov. 1901, doi: 10.1080/14786440109462720.
A. E. Maxwell, T. A. Warner, and F. Fang, “Implementation of machine-learning classification in Remote Sensing: An applied review,” International Journal of Remote Sensing, vol. 39, no. 9, pp. 2784–2817, Feb. 2018, doi: 10.1080/01431161.2018.1433343.
W. Jia, M. Sun, J. Lian, and S. Hou, "Feature dimensionality reduction: a review," Complex & Intelligent Systems, vol. 8, 2022, doi: 10.1007/s40747-021-00637-x.
W. Chen, X. Chen, and Y. Lin, "Homogeneous Ensemble Extreme Learning machine autoencoder with mutual representation learning and manifold regularization for medical datasets," Applied Intelligence, vol. 53, no. 12, pp. 15476–15495, 2022, doi: 10.1007/s10489-022-04284-8.
K. Sun, J. Zhang, C. Zhang, and J. Hu, "Generalized extreme learning machine autoencoder and a new deep neural network," Neurocomputing, vol. 230, pp. 374–381, 2017, doi: 10.1016/j.neucom.2016.12.027.
G. E. Hinton and R. R. Salakhutdinov, "Reducing the dimensionality of data with Neural Networks," Science, vol. 313, no. 5786, pp. 504–507, 2006, doi: 10.1126/science.1127647.
J. Cai, S. Wang, and W. Guo, "Unsupervised embedded feature learning for deep clustering with stacked sparse auto-encoder," Expert Systems with Applications, vol. 186, p. 115729, 2021, doi: 10.1016/j.eswa.2021.115729.
Y. Zhu et al., "Representation learning with deep sparse auto-encoder for multi-task learning," Pattern Recognition, vol. 129, p. 108742, 2022, doi: 10.1016/j.patcog.2022.108742.
P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, and P.-A. Manzagol, "Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion," Journal of Machine Learning Research, vol. 11, pp. 3371-3408, 2010.
G.-B. Huang, Q.-Y. Zhu, and C.-K. Siew, "Extreme learning machine: Theory and applications," Neurocomputing, vol. 70, no. 1–3, pp. 489–501, 2006, doi: 10.1016/j.neucom.2005.12.126.
T. Liu, C. K. L. Lekamalage, G.-B. Huang, and Z. Lin, "Extreme learning machine for joint embedding and clustering," Neurocomputing, vol. 277, pp. 78–88, 2018, doi: 10.1016/j.neucom.2017.01.115.
H. Yongyong and Z. Xiaoqiang, "Sparse representation preserving embedding based on extreme learning machine for process monitoring," Transactions of the Institute of Measurement and Control, vol. 42, no. 10, pp. 1895–1907, 2020, doi: 10.1177/0142331219898937.
L. Kasun, H. Zhou, G.-B. Huang, and C.-M. Vong, "Representational Learning with ELMs for Big Data," IEEE Intelligent Systems, vol. 28, pp. 31–34, 2013.
C. M. Wong, C. M. Vong, P. K. Wong, and J. Cao, "Kernel-Based Multilayer Extreme Learning Machines for Representation Learning," in IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 3, pp. 757-762, March 2018, doi: 10.1109/TNNLS.2016.2636834.
M. Eshtay, H. Faris, and N. Obeid, "Metaheuristic-based extreme learning machines: a review of design formulations and applications," International Journal of Machine Learning and Cybernetics, vol. 10, 2019, doi: 10.1007/s13042-018-0833-6.
M. Gholamghasemi, E. Akbari, M. Asadpoor, and M. Ghasemi, "A new solution to the non-convex economic load dispatch problems using phasor particle swarm optimization," Applied Soft Computing, vol. 79, 2019, doi: 10.1016/j.asoc.2019.03.038.
J. Rashid, S. M. Shah, and A. Irtaza, “An efficient topic modeling approach for text mining and information retrieval through K-means clustering,” Journal of Engineering & Technology, vol. 39, no. 1, pp. 213–222, Jan. 2020, doi: 10.22581/muet1982.2001.20.
M. Alhawarat and M. Hegazi, “Revisiting K-means and topic modeling, a comparison study to cluster Arabic documents,” IEEE Access, vol. 6, pp. 42740–42749, 2018, doi: 10.1109/access.2018.2852648.
T. Joachims, "A probabilistic analysis of the Rocchio algorithm with TFIDF for text categorization," Proc. 14th Int. Conf. Mach. Learn., pp. 143-151, 1997.
D. Yan, K. Li, S. Gu, and L. Yang, "Network-Based Bag-of-Words Model for Text Classification," in IEEE Access, vol. 8, pp. 82641-82652, 2020, doi: 10.1109/ACCESS.2020.2991074.
E. M. Rinke, T. Dobbrick, C. Löb, C. Zirn, and H. Wessler, "Expert-informed topic models for document set Discovery," Communication Methods and Measures, vol. 16, no. 1, pp. 39–58, 2021, doi: 10.1080/19312458.2021.1920008.
L. Xiang, "Application of an improved TF-IDF method in literary text classification," Advances in Multimedia, vol. 2022, pp. 1–10, 2022, doi: 10.1155/2022/9285324
R. Koragoankar, V. Kulkarni, and D. Naik, "Search Engine Using NLP Text Processing Techniques to Extract Most Relevant Search Results," 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT), pp. 1-7, 2023, doi: 10.1109/ICCCNT56998.2023.10307392.
R. C. Belwal, S. Rai, and A. Gupta, "Text summarization using topic-based vector space model and semantic measure," Information Processing & Management, vol. 58, no. 3, p. 102536, 2021, doi: 10.1016/j.ipm.2021.102536.
B. P. Bhopale and A. Tiwari, "Leveraging Neural Network Phrase Embedding Model For Query Reformulation In Ad-Hoc Biomedical Information Retrieval," Malaysian Journal of Computer Science, vol. 34, no. 2, pp. 151–170, Apr. 2021, doi: 10.22452/mjcs.vol34no2.2.
Y. Wu, X. Wang, W. Zhao, and X. Lv, "A novel topic clustering algorithm based on graph neural network for question topic diversity," Information Sciences, vol. 629, pp. 685–702, 2023, doi: 10.1016/j.ins.2023.02.018.
M. W. Akram et al., "A novel deep auto-encoder based linguistics clustering model for social text," ACM Transactions on Asian and Low-Resource Language Information Processing, 2022, doi: 10.1145/3527838.
Y. Guo, R. Fei, K. Zhang, Y. Tang, and B. Hu, "Developing a Clustering Structure with Consideration of Cross-Domain Text Classification based on Deep Sparse Auto-encoder," 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 2477-2483, 2020, doi: 10.1109/BIBM49941.2020.9313537.
C. Sun et al., "A Deep Learning Approach With Deep Contextualized Word Representations for Chemical–Protein Interaction Extraction From Biomedical Literature," in IEEE Access, vol. 7, pp. 151034-151046, 2019, doi: 10.1109/ACCESS.2019.2948155.
S. A. Curiskis, B. Drake, T. R. Osborn, and P. J. Kennedy, "An evaluation of document clustering and topic modelling in two online social networks: Twitter and reddit," Information Processing & Management, vol. 57, no. 2, p. 102034, 2020, doi: 10.1016/j.ipm.2019.04.002.
Y. Liu et al., "RoBERTa: A robustly optimized BERT pretraining approach," arXiv preprint arXiv:1907.11692, 2019.
T. B. Brown et al., "Language models are few-shot learners," in Proc. 34th Int. Conf. Neural Inf. Process. Syst. (NIPS'20), pp. 1–25, 2020.
C. Raffel et al., "Exploring the limits of transfer learning with a unified text-to-text transformer," Journal of Machine Learning Research, vol. 21, no. 1, pp. 5485–5551, 2020.
P. He, X. Liu, J. Gao, and W. Chen, "DeBERTa: Decoding-enhanced BERT with Disentangled Attention," arXiv preprint arXiv:2006.03654, 2020, doi: 10.48550/arXiv.2006.03654.
Z. Dai and J. Callan, "Deeper text understanding for IR with contextual neural language modeling," Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 985-988, 2019, doi: 10.1145/3331184.3331303.
X. Ma et al., "B-PROP: bootstrapped pre-training with representative words prediction for ad-hoc retrieval,” in Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1513-1522, 2021, doi: 10.1145/3404835.3462869.
Q. Liu et al., "Sgat: A self-supervised graph attention network for biomedical relation extraction," 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2021, doi: 10.1109/bibm52615.2021.9669699.
L. Huang, P. Shi, H. Zhu, and T. Chen, "Early detection of emergency events from social media: A new text clustering approach," Natural Hazards, vol. 111, no. 1, pp. 851–875, 2022. doi:10.1007/s11069-021-05081-1.
Y. Zhou, C. Li, S. He, X. Wang, and Y. Qiu, "Pre-trained contextualized representation for Chinese conversation topic classification," 2019 IEEE International Conference on Intelligence and Security Informatics (ISI), 2019, doi: 10.1109/isi.2019.8823172.
B. Chen, Z. Xu, X. Wang, L. Xu, and W. Zhang, "Capsule Network-based text sentiment classification," IFAC-PapersOnLine, vol. 53, no. 5, pp. 698–703, 2020, doi: 10.1016/j.ifacol.2021.04.160.
D. Yamunathangam, C. B. Priya, G. Shobana, and L. Latha, "An Overview of Topic Representation and Topic Modelling Methods for Short Texts and Long Corpus," 2021 International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), pp. 1-6, 2021, doi: 10.1109/ICAECA52838.2021.9675579.
B. Chen, L. Fan, and X. Fu, "Sentiment Classification of Tourism Based on Rules and LDA Topic Model," 2019 International Conference on Electronic Engineering and Informatics (EEI), pp. 471-475, 2019, doi: 10.1109/EEI48997.2019.00108.
A. -G. Văduva, M. Munteanu, S. -V. Oprea, A. Bâra, and A. -M. Niculae, "Understanding Climate Change and Air Quality Over the Last Decade: Evidence From News and Weather Data Processing," in IEEE Access, vol. 11, pp. 144631-144648, 2023, doi: 10.1109/ACCESS.2023.3345466.
Q. Xie, X. Zhang, Y. Ding, and M. Song, "Monolingual and multilingual topic analysis using LDA and Bert Embeddings," Journal of Informetrics, vol. 14, no. 3, p. 101055, 2020, doi: 10.1016/j.joi.2020.101055.
E. Atagun, B. Hartoka, and A. Albayrak, "Topic modeling using LDA and Bert Techniques: Teknofest example," 2021 6th International Conference on Computer Science and Engineering (UBMK), 2021, doi: 10.1109/ubmk52708.2021.9558988.
I. Ali and M. A. Naeem, "Identifying and Profiling User Interest over time using Social Data," 2022 24th International Multitopic Conference (INMIC), pp. 1-6, 2022, doi: 10.1109/INMIC56986.2022.9972955.
K. Kukushkin, Y. Ryabov, and A. Borovkov, "Digital Twins: A systematic literature review based on data analysis and Topic modeling," Data, vol. 7, no. 12, p. 173, 2022, doi: 10.3390/data7120173.
R. S. Nambiar and D. Gupta, "Dedicated Farm-Haystack Question Answering System for Pregnant Women and Neonates Using Corona Virus Literature," 2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence), pp. 222-227, 2022, doi: 10.1109/Confluence52989.2022.9734125.
M. Khder, "Web scraping or web crawling: State of Art, Techniques, approaches and application," International Journal of Advances in Soft Computing and its Applications, vol. 13, no. 3, pp. 145–168, 2021, doi: 10.15849/ijasca.211128.11.
D. M. Blei, "Probabilistic topic models," Communications of the ACM, vol. 55, no. 4, pp. 77–84, 2012, doi: 10.1145/2133806.2133826.
U. Chauhan and A. Shah, "Topic modeling using latent Dirichlet allocation," ACM Computing Surveys, vol. 54, no. 7, pp. 1–35, 2021, doi: 10.1145/3462478.
C. Li et al., “Mining Dynamics of research topics based on the combined LDA and WordNet,” IEEE Access, vol. 7, pp. 6386–6399, 2019, doi: 10.1109/access.2018.2887314.
D. Mimno, H. Wallach, E. Talley, M. Leenders, and A. Mccallum, "Optimizing Semantic Coherence in Topic Models," in EMNLP 2011 - Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference, pp. 262–272, 2011.
J. Devlin, M. W. Chang, K. Lee, and K. Toutanova, "Bert: Pre-training of deep bidirectional transformers for language understanding", arXiv preprint arXiv:1810.04805, 2018.
A. Vaswani et al., "Attention is all you need," in Advances in Neural Information Processing Systems, pp. 6000-6010, 2017.
A. Karimi, L. Rossi, and A. Prati, "Adversarial Training for Aspect-Based Sentiment Analysis with BERT," 2020 25th International Conference on Pattern Recognition (ICPR), pp. 8797-8803, 2021, doi: 10.1109/ICPR48806.2021.9412167.
S. Lacoste-Julien, F. Sha, and M. Jordan, "DiscLDA: Discriminative Learning for Dimensionality Reduction and Classification," in Advances in Neural Information Processing Systems, pp. 897-904, 2008.
J. A. Lossio-Ventura, J. Morzan, H. Alatrista-Salas, T. Hernandez-Boussard, and J. Bian, "Clustering and topic modeling over tweets: A comparison over a health dataset," 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1544-1547, 2019, doi: 10.1109/BIBM47256.2019.8983167.
G. Tang, X. Chen, N. Li, and J. Cui, “Research on the evolution of journal topic mining based on the bert-LDA model,” SHS Web of Conferences, vol. 152, p. 03012, 2023, doi: 10.1051/shsconf/202315203012.
B. Deng, X. Zhang, W. Gong, and D. Shang, "An Overview of Extreme Learning Machine," 2019 4th International Conference on Control, Robotics and Cybernetics (CRC), pp. 189-195, 2019, doi: 10.1109/CRC.2019.00046.
O. Ali, Q. Abbas, K. Mahmood, E. Thompson, J. Arambarri, and I. Ashraf, "Competitive Coevolution-Based Improved Phasor Particle Swarm Optimization Algorithm for Solving Continuous Problems," Mathematics, vol. 11, p. 4406, 2023, doi: 10.3390/math11214406.
M. Ghasemi et al., "Phasor particle swarm optimization: A simple and efficient variant of PSO," Soft Computing, vol. 23, no. 19, pp. 9701–9718, 2018, doi: 10.1007/s00500-018-3536-8.
M. Gajić, M. Jevtic, J. Radosavljević, S. Arsic, and D. Klimenta, "Phasor Particle Swarm Optimization for Solving Problem of Pricing in Electricity Market," in International Scientific Conference “UNITECH, vol. 1, p. 257, 2021.
D. El Bourakadi, A. Yahyaouy, and J. Boumhidi, "Improved extreme learning machine with AutoEncoder and particle swarm optimization for short-term wind power prediction," Neural Computing and Applications, vol. 34, pp. 1–17, 2022, doi: 10.1007/s00521-021-06619-x.
G. H. de Rosa, J. R. Brega, and J. P. Papa, “How optimizing perplexity can affect the dimensionality reduction on word embeddings visualization?” SN Applied Sciences, vol. 1, no. 12, Nov. 2019, doi: 10.1007/s42452-019-1689-4.
Z. Tian, Y. Ren, and G. Wang, "Short-term wind speed prediction based on improved PSO algorithm optimized EM-ELM," Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, vol. 41, pp. 26–46, 2019.
Y. Wang, D. Wang, and Y. Tang, "Clustered Hybrid Wind Power Prediction Model Based on ARMA, PSO-SVM, and Clustering Methods," in IEEE Access, vol. 8, pp. 17071-17079, 2020, doi: 10.1109/ACCESS.2020.2968390.
M. Srinivas and L. M. Patnaik, "Genetic algorithms: a survey," in Computer, vol. 27, no. 6, pp. 17-26, 1994, doi: 10.1109/2.294849.
M. Ahmed, R. Seraj, and S. M. Islam, "The K-Means Algorithm: A comprehensive survey and performance evaluation," Electronics, vol. 9, no. 8, p. 1295, 2020, doi:10.3390/electronics9081295.
K. Sethia, M. Saxena, M. Goyal, and R. K. Yadav, "Framework for Topic Modeling using BERT, LDA and K-Means," 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), pp. 2204-2208, 2022, doi: 10.1109/ICACITE53722.2022.9823442.
L. George and P. Sumathy, "An Integrated Clustering and BERT Framework for Improved Topic Modeling," pp. 1-9, 2022, doi: 10.21203/rs.3.rs-1986180/v1.
T. Dinh, T. Fujinami, and V.-N. Huynh, "Estimating the Optimal Number of Clusters in Categorical Data Clustering by Silhouette Coefficient," in Proceedings of the International Conference on Advanced Technologies for Communications, pp. 1-1, 2019, doi: 10.1007/978-981-15-1209-4_1.
K. R. Shahapure and C. Nicholas, "Cluster Quality Analysis Using Silhouette Score," 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), pp. 747-748, 2020, doi: 10.1109/DSAA49011.2020.00096.
A. Naghizadeh and D. N. Metaxas, "Condensed silhouette: An optimized filtering process for cluster selection in K-means," in Procedia Computer Science, vol. 176, pp. 205-214, 2020.
I. Bouabdallaoui, F. Guerouate, and M. Sbihi, "Combination of genetic algorithms and k-means for a hybrid topic modeling: Tourism use case," Evolutionary Intelligence, pp. 1-17, 2023, doi: 10.1007/s12065-023-00863-x.
D. Marutho, S. H. Handaka, E. Wijaya, and Muljono, "The Determination of Cluster Number at k-Mean Using Elbow Method and Purity Evaluation on Headline News," 2018 International Seminar on Application for Technology of Information and Communication, pp. 533-538, 2018, doi: 10.1109/ISEMANTIC.2018.8549751.
L. Liu et al., "Fast identification of urban sprawl based on K-means clustering with population density and local spatial entropy," Sustainability, vol. 10, no. 8, p. 2683, 2018, doi: 10.3390/su10082683.
A. H. Marani and E. P. Baumer, "A review of stability in topic modeling: Metrics for assessing and techniques for improving stability," ACM Computing Surveys, vol. 56, no. 5, pp. 1–32, 2023, doi: 10.1145/3623269.
A. El-Hamdouchi, "Comparison of hierarchic agglomerative clustering methods for document retrieval," The Computer Journal, vol. 32, no. 3, pp. 220–227, 1989, doi: 10.1093/comjnl/32.3.220.
B. Ghojogh, M. Crowley, F. Karray, and A. Ghodsi, “Uniform manifold approximation and projection (UMAP),” Elements of Dimensionality Reduction and Manifold Learning, pp. 479–497, 2023, doi: 10.1007/978-3-031-10602-6_17.
DOI: https://doi.org/10.18196/jrc.v5i2.21419
Refbacks
- There are currently no refbacks.
Copyright (c) 2024 Thinzar Aung Win, Khamron Sunat
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Journal of Robotics and Control (JRC)
P-ISSN: 2715-5056 || E-ISSN: 2715-5072
Organized by Peneliti Teknologi Teknik Indonesia
Published by Universitas Muhammadiyah Yogyakarta in collaboration with Peneliti Teknologi Teknik Indonesia, Indonesia and the Department of Electrical Engineering
Website: http://journal.umy.ac.id/index.php/jrc
Email: jrcofumy@gmail.com