Relevant bibliographies by topics / Multinomial dirichlet classification model

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Academic literature on the topic 'Multinomial dirichlet classification model'

Author: Grafiati
Published: 3 June 2025
Last updated: 19 July 2025

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Journal articles on the topic "Multinomial dirichlet classification model"

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de Waal, Daan, Roelof Coetzer, and Sean van der Merwe. "Classification of multiple Dirichlet observations under a Multinomial Model." Chemometrics and Intelligent Laboratory Systems 150 (January 2016): 51–57. http://dx.doi.org/10.1016/j.chemolab.2015.10.012.

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Abdul Rahman, Teh Faradilla, Norshita Mat Nayan, Nurhilyana Anuar, and Aminatul Solehah Idris. "Dirichlet Multinomial Modelling Approaches in Analyzing Anxiety Therapy Messages." Journal of Information and Knowledge Management 15, no. 1 (2025): 98–108. https://doi.org/10.24191/jikm.v15i1.4541.

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Despite the effectiveness of anxiety therapy through text messages, limited research was found to analyse the topics included in the therapy session. It is also unclear of which topic modelling approaches is the best in extracting anxiety therapy topics from text messages. Thus, this study aims to compare the performance of four topic modelling methods, namely Latent Feature Di-richlet Multinomial Mixture (LFDMM), Gibbs Sampling Dirichlet Multinomi-al Mixture, Generalized Polya-urn Dirichlet Multinomial Mixture and Pois-son-based Dirichlet Multinomial Mixture Model on 28 text messages of anxi-
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Kudrekar, Sheelavathy Veerabhadrappa, and Udaya Rani Vinayakamurthy. "Classification of malware using multinomial linked latent modular double q learning." Indonesian Journal of Electrical Engineering and Computer Science 28, no. 1 (2022): 577. http://dx.doi.org/10.11591/ijeecs.v28.i1.pp577-586.

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In recent times, malware has progressed by utilizing distinct advanced machine learning techniques for detection. However, the model becomes complicated and the singular value decomposition and depth-based malware detectors failed to detect the malware significantly with minimum time and overhead. This paper proposes a multinomial linked latent dirichlet and modular double q learning (MLLD-MDQL) to efficiently detect malware based on the network behavior patterns. First, multinomial linked latent dirichlet network behavior extraction (ML-LDNBE) is applied to the input network for anomaly detec
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Kudrekar, Sheelavathy Veerabhadrappa, and Udaya Rani Vinayaka Murthy. "Classification of malware using multinomial linked latent modular double q learning." Indonesian Journal of Electrical Engineering and Computer Science 28, no. 1 (2022): 577–86. https://doi.org/10.11591/ijeecs.v28.i1.pp577-586.

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In recent times, malware has progressed by utilizing distinct advanced machine learning techniques for detection. However, the model becomes complicated and the singular value decomposition and depth-based malware detectors failed to detect the malware significantly with minimum time and overhead. This paper proposes a multinomial linked latent dirichlet and modular double q learning (MLLD-MDQL) to efficiently detect malware based on the network behavior patterns. First, multinomial linked latent dirichlet network behavior extraction (ML-LDNBE) is applied to the input network for anomaly detec
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Frimane, Âzeddine, Mohammed Aggour, Badr Ouhammou, and Lahoucine Bahmad. "A Dirichlet-multinomial mixture model-based approach for daily solar radiation classification." Solar Energy 171 (September 2018): 31–39. http://dx.doi.org/10.1016/j.solener.2018.06.059.

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Mazarura, Jocelyn, Alta de Waal, and Pieter de Villiers. "A Gamma-Poisson Mixture Topic Model for Short Text." Mathematical Problems in Engineering 2020 (April 29, 2020): 1–17. http://dx.doi.org/10.1155/2020/4728095.

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Most topic models are constructed under the assumption that documents follow a multinomial distribution. The Poisson distribution is an alternative distribution to describe the probability of count data. For topic modelling, the Poisson distribution describes the number of occurrences of a word in documents of fixed length. The Poisson distribution has been successfully applied in text classification, but its application to topic modelling is not well documented, specifically in the context of a generative probabilistic model. Furthermore, the few Poisson topic models in the literature are adm
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Ogura, Hiroshi, Hiromi Amano, and Masato Kondo. "Gamma-Poisson Distribution Model for Text Categorization." ISRN Artificial Intelligence 2013 (April 4, 2013): 1–17. http://dx.doi.org/10.1155/2013/829630.

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We introduce a new model for describing word frequency distributions in documents for automatic text classification tasks. In the model, the gamma-Poisson probability distribution is used to achieve better text modeling. The framework of the modeling and its application to text categorization are demonstrated with practical techniques for parameter estimation and vector normalization. To investigate the efficiency of our model, text categorization experiments were performed on 20 Newsgroups, Reuters-21578, Industry Sector, and TechTC-100 datasets. The results show that the model allows perform
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Koochemeshkian, Pantea, Eddy Ihou Koffi, and Nizar Bouguila. "Hidden Variable Models in Text Classification and Sentiment Analysis." Electronics 13, no. 10 (2024): 1859. http://dx.doi.org/10.3390/electronics13101859.

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In this paper, we are proposing extensions to the multinomial principal component analysis (MPCA) framework, which is a Dirichlet (Dir)-based model widely used in text document analysis. The MPCA is a discrete analogue to the standard PCA (it operates on continuous data using Gaussian distributions). With the extensive use of count data in modeling nowadays, the current limitations of the Dir prior (independent assumption within its components and very restricted covariance structure) tend to prevent efficient processing. As a result, we are proposing some alternatives with flexible priors suc
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Wong, Tzu-Tsung. "Generalized Dirichlet priors for Naïve Bayesian classifiers with multinomial models in document classification." Data Mining and Knowledge Discovery 28, no. 1 (2012): 123–44. http://dx.doi.org/10.1007/s10618-012-0296-4.

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Maku, T. O., M. O. Adenomon, M. U. Adehi, and S. E. Chaku. "Reliability of supervised topic models over unsupervised topic models for the prediction task." Science World Journal 19, no. 4 (2025): 959–67. https://doi.org/10.4314/swj.v19i4.8.

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The study investigated the depth of machine learning's capacity to perform prediction tasks. The study used textual data, specifically the daily actions of cryptocurrency (Bitcoin) dealers, which were found in news articles. The data was employed merely because it produced crowd knowledge of trade from News articles that affected the market price trend. For the goal of making predictions, 4073 pre-processed, scraped news articles from CNBC's market section website were analysed using the Latent Dirichlet Allocation (LDA) model and its variation, the Supervised Latent Dirichlet Allocation Model
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Dissertations / Theses on the topic "Multinomial dirichlet classification model"

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Järvstråt, Lotta. "Functionality Classification Filter for Websites." Thesis, Linköpings universitet, Statistik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-93702.

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The objective of this thesis is to evaluate different models and methods for website classification. The websites are classified based on their functionality, in this case specifically whether they are forums, news sites or blogs. The analysis aims at solving a search engine problem, which means that it is interesting to know from which categories in a information search the results come. The data consists of two datasets, extracted from the web in January and April 2013. Together these data sets consist of approximately 40.000 observations, with each observation being the extracted text from
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Monson, Rebecca Lee. "Modeling Transition Probabilities for Loan States Using a Bayesian Hierarchical Model." Diss., CLICK HERE for online access, 2007. http://contentdm.lib.byu.edu/ETD/image/etd2179.pdf.

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Bakhtiari, Koohsorkhi Alireza. "Analysis of the Dirichlet Process Mixture Model with Application to Dialogue Act Classification." Thesis, Université Laval, 2011. http://www.theses.ulaval.ca/2011/28503/28503.pdf.

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La reconnaissance des intentions de l’utilisateur est l’un des problèmes les plus difficiles dans la conception des systèmes de dialogues. Ces intentions sont généralement codés en termes d’actes de dialogue, où un rôle fonctionnel est attribué à chaque énoncé d’une conversation. L’annotation manuelle des actes de dialogue est généralement coûteuse et prends du temps, il y a donc un grand intérêt à plutôt annoter automatiquement des corpus de dialogue. Dans ce mémoire, nous proposons une approche non paramétrique bayésienne pour
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Frühwirth-Schnatter, Sylvia, and Gertraud Malsiner-Walli. "From here to infinity: sparse finite versus Dirichlet process mixtures in model-based clustering." Springer Berlin Heidelberg, 2019. http://dx.doi.org/10.1007/s11634-018-0329-y.

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In model-based clustering mixture models are used to group data points into clusters. A useful concept introduced for Gaussian mixtures by Malsiner Walli et al. (Stat Comput 26:303-324, 2016) are sparse finite mixtures, where the prior distribution on the weight distribution of a mixture with K components is chosen in such a way that a priori the number of clusters in the data is random and is allowed to be smaller than K with high probability. The number of clusters is then inferred a posteriori from the data. The present paper makes the following contributions in the context of sparse finite
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Hrabák, Jan. "Analýza sentimentu zákaznických recenzí." Master's thesis, Vysoká škola ekonomická v Praze, 2016. http://www.nusl.cz/ntk/nusl-264264.

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This thesis is focused on sentiment analysis of unstructured text and its practical application on the real data downloaded from website Yelp.com The objectives of the theoretical part of this thesis is to sum up the information related to history, methods and possible applications of sentiment analysis. A reader is acquainted with important terms and processes of sentiment analysis. Theoretical part is focused on Naive Bayes classifier, that will be used in practical part of this thesis. In practical part there is detailed description of data set, construction and testing of model. At the end
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Byrne, Evan. "Inference in Generalized Linear Models with Applications." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1555152640361367.

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Wei, Zhihua. "The research on chinese text multi-label classification." Thesis, Lyon 2, 2010. http://www.theses.fr/2010LYO20025/document.

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Text Classification (TC) which is an important field in information technology has many valuable applications. When facing the sea of information resources, the objects of TC are more complicated and diversity. The researches in pursuit of effective and practical TC technology are fairly challenging. More and more researchers regard that multi-label TC is more suited for many applications. This thesis analyses the difficulties and problems in multi-label TC and Chinese text representation based on a mass of algorithms for single-label TC and multi-label TC. Aiming at high dimensionality in fea
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Sievert, Rolf. "Instance Segmentation of Multiclass Litter and Imbalanced Dataset Handling : A Deep Learning Model Comparison." Thesis, Linköpings universitet, Datorseende, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-175173.

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Instance segmentation has a great potential for improving the current state of littering by autonomously detecting and segmenting different categories of litter. With this information, litter could, for example, be geotagged to aid litter pickers or to give precise locational information to unmanned vehicles for autonomous litter collection. Land-based litter instance segmentation is a relatively unexplored field, and this study aims to give a comparison of the instance segmentation models Mask R-CNN and DetectoRS using the multiclass litter dataset called Trash Annotations in Context (TACO) i
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Bartcus, Marius. "Bayesian non-parametric parsimonious mixtures for model-based clustering." Thesis, Toulon, 2015. http://www.theses.fr/2015TOUL0010/document.

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Cette thèse porte sur l’apprentissage statistique et l’analyse de données multi-dimensionnelles. Elle se focalise particulièrement sur l’apprentissage non supervisé de modèles génératifs pour la classification automatique. Nous étudions les modèles de mélanges Gaussians, aussi bien dans le contexte d’estimation par maximum de vraisemblance via l’algorithme EM, que dans le contexte Bayésien d’estimation par Maximum A Posteriori via des techniques d’échantillonnage par Monte Carlo. Nous considérons principalement les modèles de mélange parcimonieux qui reposent sur une décomposition spectrale de
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Corneli, Marco. "Dynamic stochastic block models, clustering and segmentation in dynamic graphs." Thesis, Paris 1, 2017. http://www.theses.fr/2017PA01E012/document.

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Cette thèse porte sur l’analyse de graphes dynamiques, définis en temps discret ou continu. Nous introduisons une nouvelle extension dynamique du modèle a blocs stochastiques (SBM), appelée dSBM, qui utilise des processus de Poisson non homogènes pour modéliser les interactions parmi les paires de nœuds d’un graphe dynamique. Les fonctions d’intensité des processus ne dépendent que des classes des nœuds comme dans SBM. De plus, ces fonctions d’intensité ont des propriétés de régularité sur des intervalles temporels qui sont à estimer, et à l’intérieur desquels les processus de Poisson redevien
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Books on the topic "Multinomial dirichlet classification model"

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Poterba, James M. Unemployment benefits, labor market transitions, and spurious flows: A multinomial logit model with errors in classification. National Bureau of Economic Research, 1993.

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Book chapters on the topic "Multinomial dirichlet classification model"

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Cerchiello, Paola, and Elvio Concetto Bonafede. "A Semantic Based Dirichlet Compound Multinomial Model." In Studies in Classification, Data Analysis, and Knowledge Organization. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-11363-5_59.

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Cho, Wanhyun, Soonja Kang, Sangkyoon Kim, and Soonyoung Park. "Variational Bayesian Inference for Multinomial Dirichlet Gaussian Process Classification Model." In Advances in Computer Science and Ubiquitous Computing. Springer Singapore, 2015. http://dx.doi.org/10.1007/978-981-10-0281-6_117.

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Ascari, Roberto, and Sonia Migliorati. "A New Regression Model for the Analysis of Microbiome Data." In Studies in Classification, Data Analysis, and Knowledge Organization. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-09034-9_5.

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AbstractHuman microbiome data are becoming extremely common in biomedical research due to the relevant connections with different types of diseases. A widespread discrete distribution to analyze this kind of data is the Dirichletmultinomial. Despite its popularity, this distribution often fails in modeling microbiome data due to the strict parameterization imposed on its covariance matrix. The aim of this work is to propose a new distribution for analyzing microbiome data and to define a regression model based on it. The new distribution can be expressed as a structured finite mixture model with Dirichlet-multinomial components. We illustrate how this mixture structure can improve a microbiome data analysis to cluster patients into “enterotypes”, which are a classification based on the bacteriological composition of gut microbiota. The comparison between the two models is performed through an application to a real gut microbiome dataset.
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Bonafede, Concetto Elvio, and Paola Cerchiello. "A Study on Text Modelling via Dirichlet Compound Multinomial." In Classification and Multivariate Analysis for Complex Data Structures. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13312-1_11.

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Bartels, K., Y. Boztug, and M. Müller. "Testing the Multinomial Logit Model." In Studies in Classification, Data Analysis, and Knowledge Organization. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-57280-7_32.

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Hannachi, Samar, Fatma Najar, and Nizar Bouguila. "Short Text Clustering Using Generalized Dirichlet Multinomial Mixture Model." In Recent Challenges in Intelligent Information and Database Systems. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1685-3_13.

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Ghidini, Valentina. "Hierarchical Dirichlet-Multinomial Model for Clustering in Multiplex Networks." In Italian Statistical Society Series on Advances in Statistics. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-64350-7_10.

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de Cooman, Gert, Jasper De Bock, and Márcio Diniz. "Predictive Inference Under Exchangeability, and the Imprecise Dirichlet Multinomial Model." In Springer Proceedings in Mathematics & Statistics. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-12454-4_2.

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Najar, Fatma, and Nizar Bouguila. "Happiness Analysis with Fisher Information of Dirichlet-Multinomial Mixture Model." In Advances in Artificial Intelligence. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47358-7_45.

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Novovičová, Jana, and Antonín Malík. "Application of Multinomial Mixture Model to Text Classification." In Pattern Recognition and Image Analysis. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-44871-6_75.

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Conference papers on the topic "Multinomial dirichlet classification model"

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Tahsin, Faiza, Hafsa Ennajari, and Nizar Bouguila. "Author Dirichlet Multinomial Allocation Model with Generalized Distribution (ADMAGD)." In 2024 International Symposium on Networks, Computers and Communications (ISNCC). IEEE, 2024. http://dx.doi.org/10.1109/isncc62547.2024.10758998.

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Yin, Jianhua, and Jianyong Wang. "A dirichlet multinomial mixture model-based approach for short text clustering." In KDD '14: The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2014. http://dx.doi.org/10.1145/2623330.2623715.

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Azzimonti, Laura, Giorgio Corani, and Marco Zaffalon. "Hierarchical Multinomial-Dirichlet Model for the Estimation of Conditional Probability Tables." In 2017 IEEE International Conference on Data Mining (ICDM). IEEE, 2017. http://dx.doi.org/10.1109/icdm.2017.85.

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Duan, Ruting, and Chunping Li. "An Adaptive Dirichlet Multinomial Mixture Model for Short Text Streaming Clustering." In 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI). IEEE, 2018. http://dx.doi.org/10.1109/wi.2018.0-108.

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Xiao, Y., C. Lin, Y. Jiang, X. Chu, and X. Shen. "Reputation-Based QoS Provisioning in Cloud Computing via Dirichlet Multinomial Model." In ICC 2010 - 2010 IEEE International Conference on Communications. IEEE, 2010. http://dx.doi.org/10.1109/icc.2010.5502407.

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Zhou, Shibin, Zhao Cao, and Yushu Liu. "Classifying Documents with Maximum Likelihood Approximation of the Dirichlet Multinomial Gibbs Model." In 2008 Second International Symposium on Intelligent Information Technology Application (IITA). IEEE, 2008. http://dx.doi.org/10.1109/iita.2008.307.

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Xu, Zuobing, and Ram Akella. "A new probabilistic retrieval model based on the dirichlet compound multinomial distribution." In the 31st annual international ACM SIGIR conference. ACM Press, 2008. http://dx.doi.org/10.1145/1390334.1390408.

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Xinxin Fan and Guang Gong. "Key revocation based on Dirichlet multinomial model for mobile ad hoc networks." In 2008 33rd IEEE Conference on Local Computer Networks (LCN 2008). IEEE, 2008. http://dx.doi.org/10.1109/lcn.2008.4664309.

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Li, Ziyue, Hao Yan, Chen Zhang, Wolfgang Ketter, and Fugee Tsung. "Tensor Dirichlet Process Multinomial Mixture Model with Graphs for Passenger Trajectory Clustering." In SIGSPATIAL '23: The 31st ACM International Conference on Advances in Geographic Information Systems. ACM, 2023. http://dx.doi.org/10.1145/3615886.3627749.

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Mazarura, Jocelyn, and Alta de Waal. "A comparison of the performance of latent Dirichlet allocation and the Dirichlet multinomial mixture model on short text." In 2016 PRASA-RobMech International Conference. IEEE, 2016. http://dx.doi.org/10.1109/robomech.2016.7813155.

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