@article{impact-of-machine-learning-on-intrusion-detection-systems-for-the-protection-of-critical-infrastructure,
AUTHOR = {Kumar, Avinash and Gutierrez, Jairo A.},
TITLE = {Impact of Machine Learning on Intrusion Detection Systems for the Protection of Critical Infrastructure},
JOURNAL = {Information},
VOLUME = {16},
YEAR = {2025},
NUMBER = {7},
ARTICLE-NUMBER = {515},
URL = {https://www.mdpi.com/2078-2489/16/7/515},
ISSN = {2078-2489},
ABSTRACT = {In the realm of critical infrastructure protection, robust intrusion detection systems (IDSs) are essential for securing essential services. This paper investigates the efficacy of various machine learning algorithms for anomaly detection within critical infrastructure, using the Secure Water Treatment (SWaT) dataset, a comprehensive collection of time-series data from a water treatment testbed, to experiment upon and analyze the findings. The study evaluates supervised learning algorithms alongside unsupervised learning algorithms. The analysis reveals that supervised learning algorithms exhibit exceptional performance with high accuracy and reliability, making them well-suited for handling the diverse and complex nature of anomalies in critical infrastructure. They demonstrate significant capabilities in capturing spatial and temporal variables. Among the unsupervised approaches, valuable insights into anomaly detection are provided without the necessity for labeled data, although they face challenges with higher rates of false positives and negatives. By outlining the benefits and drawbacks of these machine learning algorithms in relation to critical infrastructure, this research advances the field of cybersecurity. It emphasizes the importance of integrating supervised and unsupervised techniques to enhance the resilience of IDSs, ensuring the timely detection and mitigation of potential threats. The findings offer practical guidance for industry professionals on selecting and deploying effective machine learning algorithms in critical infrastructure environments.},
DOI = {10.3390/info16070515}
}

@article{web-application-firewall-based-on-machine-learning-models,
author = {M.~E.~Durmu{\c{s}}kaya and S.~Bayrakl{\i}},
title  = {Web application firewall based on machine learning models},
journal = {PeerJ Computer Science},
year = {2025},
volume = {11},
pages = {e2975},
doi = {10.7717/peerj-cs.2975},
url = {https://doi.org/10.7717/peerj-cs.2975}
}

@article{the-threat-of-adversarial-attacks-against-machine-learning-in-network-security-a-survey,
author = {Ibitoye, Olakunle and Abou-Khamis, Rana and ElShehaby, Mohamed and Matrawy, Ashraf and Shafiq, M.},
year = {2025},
month = {01},
pages = {},
title = {The Threat of Adversarial Attacks against Machine Learning in Network Security: A Survey},
journal = {Journal of Electronics and Electrical Engineering},
doi = {10.37256/jeee.4120255738}
}

@article{machine-learning-models-in-web-applications-a-comprehensive-review,
title = {Machine learning models in web applications: A comprehensive review},
journal = {ICT Express},
year = {2025},
issn = {2405-9595},
doi = {https://doi.org/10.1016/j.icte.2025.09.001},
url = {https://www.sciencedirect.com/science/article/pii/S2405959525001304},
author = {Kshiteesh Mani and Ajitha K.B. Shenoy},
keywords = {Artificial intelligence, Machine learning, Node.js, Python, Web applications, Web application firewalls},
abstract = {The rapid growth of web applications has increased the need for advanced features and strong security. Artificial intelligence (AI) and machine learning (ML) models play a crucial role in meeting these needs by improving efficiency and enhancing security. However, integrating these models into web applications can be challenging due to complex implementation and potential security risks. This paper compares Python and Node.js, two popular technology stacks, to determine their effectiveness in integrating ML models into web applications. It also explores the role of web application firewalls (WAF) and the ML algorithms that support them, analyzing current trends in their use and adoption. The overarching objective is to discern the technology stack that provides superior support for back-end ML integration and to identify the ML algorithms that are most effective in enhancing WAF capabilities against sophisticated security threats. By offering a synthesis of technical and security insights, this research seeks to empower developers and cybersecurity practitioners with the knowledge required to make well-informed decisions regarding technology stack selection and the implementation of ML-driven security mechanisms in web application development.}
}

@article{unsupervised-clustering-of-web-sessions-to-detect-malicious-and-non-malicious-website-users,
title = {Unsupervised Clustering of Web Sessions to Detect Malicious and Non-malicious Website Users},
journal = {Procedia Computer Science},
volume = {5},
pages = {123-131},
year = {2011},
note = {The 2nd International Conference on Ambient Systems, Networks and Technologies (ANT-2011) / The 8th International Conference on Mobile Web Information Systems (MobiWIS 2011)},
issn = {1877-0509},
doi = {https://doi.org/10.1016/j.procs.2011.07.018},
url = {https://www.sciencedirect.com/science/article/pii/S1877050911003437},
author = {Dusan Stevanovic and Natalija Vlajic and Aijun An},
keywords = {Web Crawler Detection, Neural Networks, Web Server Access Logs, Machine Learning, Clustering, Denial of Service ;},
abstract = {Denial of Service (DoS) attacks are recognized as one of the most damaging attacks on the Internet security today. Recently, malicious web crawlers have been used to execute automated DoS attacks on web sites across the WWW. In this study, we examine the use of two unsupervised neural network (NN) learning algorithms for the purpose web-log analysis: the Self- Organizing Map (SOM) and Modified Adaptive Resonance Theory 2 (Modified ART2). In particular, through the use of SOM and Modified ART2, our work aims to obtain a better insight into the types and distribution of visitors to a public web-site based on their link-traversal behaviour, as well as to investigate the relative differences and/or similarities between malicious web crawlers and other non-malicious visitor groups. The results of our study show that, even though there is a pretty clear separation between malicious web-crawlers and other visitor groups, around 8% of malicious crawlers exhibit very ‘human-like’ browsing behaviour and as such pose a particular challenge for future web-site security systems.}
}


@inproceedings{wsad-an-unsupervised-web-session-anomalydetection-method,
  author={Sun, Yizhen and Xie, Yiman and Wang, Weiping and Zhang, Shigeng and Gao, Jun and Chen, Yating},
  booktitle={2020 16th International Conference on Mobility, Sensing and Networking (MSN)}, 
  title={WSAD: An Unsupervised Web Session Anomaly Detection Method}, 
  year={2020},
  volume={},
  number={},
  pages={735-739},
  keywords={Firewalls (computing);Clustering algorithms;Companies;Transcoding;Feature extraction;Servers;Anomaly detection;Anomaly detection;Web session;Unsupervised algorithm},
  doi={10.1109/MSN50589.2020.00125}}

@inproceedings{a-large-scale-measurement-study-and-classification-of-fingerprint-dynamics,
author = {Li, Song and Cao, Yinzhi},
title = {Who Touched My Browser Fingerprint? A Large-scale Measurement Study and Classification of Fingerprint Dynamics},
year = {2020},
isbn = {9781450381383},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3419394.3423614},
doi = {10.1145/3419394.3423614},
abstract = {Browser fingerprints are dynamic, evolving with feature values changed over time. Previous fingerprinting datasets are either small-scale with only thousands of browser instances or without considering fingerprint dynamics. Thus, it remains unclear how an evolution-aware fingerprinting tool behaves in a real-world setting, e.g., on a website with millions of browser instances, let alone how fingerprint dynamics implicate privacy and security.In this paper, we perform the first, large-scale study of millions of fingerprints to analyze fingerprint dynamics in a real-world website. Our measurement study answers the question of how and why fingerprints change over time by classifying fingerprint dynamics into three categories based on their causes. We also observed several insights from our measurement, e.g., we show that state-of-the-art fingerprinting tool performs poorly in terms of F1-Score and matching speed in this real-world setting.},
booktitle = {Proceedings of the ACM Internet Measurement Conference},
pages = {370--385},
numpages = {16},
location = {Virtual Event, USA},
series = {IMC '20}
}

@misc{beyond-the-crawl-unmasking-browser-fingerprinting-in-real-user-interactions,
      title={Beyond the Crawl: Unmasking Browser Fingerprinting in Real User Interactions}, 
      author={Meenatchi Sundaram Muthu Selva Annamalai and Igor Bilogrevic and Emiliano De Cristofaro},
      year={2025},
      eprint={2502.01608},
      archivePrefix={arXiv},
      primaryClass={cs.CR},
      url={https://arxiv.org/abs/2502.01608}, 
}

@misc{fp-inconsistent-measurement-and-analysis-of-fingerprint-inconsistencies-in-evasive-bot-traffic,
      title={FP-Inconsistent: Measurement and Analysis of Fingerprint Inconsistencies in Evasive Bot Traffic}, 
      author={Hari Venugopalan and Shaoor Munir and Shuaib Ahmed and Tangbaihe Wang and Samuel T. King and Zubair Shafiq},
      year={2025},
      eprint={2406.07647},
      archivePrefix={arXiv},
      primaryClass={cs.CR},
      url={https://arxiv.org/abs/2406.07647}, 
}

@inproceedings{tales-of-favicons-and-caches-persistent-tracking-in-modern-browsers,
author = {Solomos, Konstantinos and Kristoff, John and Kanich, Chris and Polakis, Jason},
booktitle = {Proceedings of the Network and Distributed System Security Symposium (NDSS)},
year = {2021},
month = {01},
pages = {},
title = {Tales of Favicons and Caches: Persistent Tracking in Modern Browsers},
doi = {10.14722/ndss.2021.24202}
}

@mastersthesis{botsbfuod-web-bot-detection-using-biometric-features-and-unsupervised-outlier-detection,
    author = {Pedro C. Pereira},
    title = {Web Bot Detection using Biometric Features and Unsupervised Outlier Detection},
    note  = {Available at \url{https://repositorio-aberto.up.pt/bitstream/10216/145527/2/592224.pdf}},
    year = {2022},
    month = {October},
    school = {Universidade Do Porto},
    type  = {Master's thesis}
}

%  REMOVE %
%  REMOVE %
%  REMOVE %

@article{web-robot-detection-a-probabilistic-reasoning-approach,
title = {Web robot detection: A probabilistic reasoning approach},
journal = {Computer Networks},
volume = {53},
number = {3},
pages = {265-278},
year = {2009},
issn = {1389-1286},
doi = {https://doi.org/10.1016/j.comnet.2008.09.021},
url = {https://www.sciencedirect.com/science/article/pii/S1389128608003174},
author = {Athena Stassopoulou and Marios D. Dikaiakos},
keywords = {Web crawler detection, Probabilistic reasoning, Bayesian classifiers},
abstract = {In this paper, we introduce a probabilistic modeling approach for addressing the problem of Web robot detection from Web-server access logs. More specifically, we construct a Bayesian network that classifies automatically access log sessions as being crawler- or human-induced, by combining various pieces of evidence proven to characterize crawler and human behavior. Our approach uses an adaptive-threshold technique to extract Web sessions from access logs. Then, we apply machine learning techniques to determine the parameters of the probabilistic model. The resulting classification is based on the maximum posterior probability of all classes given the available evidence. We apply our method to real Web-server logs and obtain results that demonstrate the robustness and effectiveness of probabilistic reasoning for crawler detection.}
}

@article{feature-selection-techniques-for-machine-learning-a-survey-of-more-than-two-decades-of-research,
author = {Theng, Dipti and Bhoyar, Kishor K.},
title = {Feature selection techniques for machine learning: a survey of more than two decades of research},
year = {2023},
issue_date = {Mar 2024},
publisher = {Springer-Verlag},
address = {Berlin, Heidelberg},
volume = {66},
number = {3},
issn = {0219-1377},
url = {https://doi.org/10.1007/s10115-023-02010-5},
doi = {10.1007/s10115-023-02010-5},
abstract = {Learning algorithms can be less effective on datasets with an extensive feature space due to the presence of irrelevant and redundant features. Feature selection is a technique that effectively reduces the dimensionality of the feature space by eliminating irrelevant and redundant features without significantly affecting the quality of decision-making of the trained model. In the last few decades, numerous algorithms have been developed to identify the most significant features for specific learning tasks. Each algorithm has its advantages and disadvantages, and it is the responsibility of a data scientist to determine the suitability of a specific algorithm for a particular task. However, with the availability of a vast number of feature selection algorithms, selecting the appropriate one can be a daunting task for an expert. These challenges in feature selection have motivated us to analyze the properties of algorithms and dataset characteristics together. This paper presents significant efforts to review existing feature selection algorithms, providing an exhaustive analysis of their properties and relative performance. It also addresses the evolution, formulation, and usefulness of these algorithms. The manuscript further categorizes the algorithms analyzed in this review based on the properties required for a specific dataset and objective under study. Additionally, it discusses popular area-specific feature selection techniques. Finally, it identifies and discusses some open research challenges in feature selection that are yet to be overcome.},
journal = {Knowl. Inf. Syst.},
month = dec,
pages = {1575-1637},
numpages = {63},
keywords = {Feature selection, Machine learning, High-dimensional data, Filter techniques, Wrapper techniques, Embedded techniques}
}

@article{feature-selection-a-data-perspective,
title={Feature selection: A data perspective},
author={Li, Jundong and Cheng, Kewei and Wang, Suhang and Morstatter, Fred and Trevino, Robert P and Tang, Jiliang and Liu, Huan},
journal={ACM Computing Surveys (CSUR)},
volume={50},
number={6},
pages={94},
year={2018},
publisher={ACM}
}

@article{an-overview-on-deep-clustering,
title = {An overview on deep clustering},
journal = {Neurocomputing},
volume = {590},
pages = {127761},
year = {2024},
issn = {0925-2312},
doi = {https://doi.org/10.1016/j.neucom.2024.127761},
url = {https://www.sciencedirect.com/science/article/pii/S0925231224005320},
author = {Xiuxi Wei and Zhihui Zhang and Huajuan Huang and Yongquan Zhou},
keywords = {Clustering, Deep Clustering, Unsupervised Learning, Representation Learning, Deep Learning},
abstract = {In recent years, with the great success of deep learning and especially deep unsupervised learning, many deep architectural clustering methods, collectively known as deep clustering, have emerged. Deep clustering shows the potential to outperform traditional methods, especially in handling complex high-dimensional data, taking full advantage of deep learning. To achieve a comprehensive overview of the field of deep clustering, this review systematically explores deep clustering methods and their various applications. First, the basic network architecture of deep clustering is described in detail, including the common network frameworks, and loss functions. Subsequently, deep clustering is divided into several categories based on the network architecture, and benchmark datasets and evaluation metrics in the field are introduced. Next, the real-world applications of deep clustering are explored in depth, providing successful cases in the fields of bioinformatics, medicine, anomaly detection, and image processing, highlighting the broad applicability of deep clustering in solving real-world challenges. Finally, the paper summarizes its contributions and explores potential directions for future research in deep clustering.}
}

@ARTICLE{deep-clustering-a-comprehensive-review,
  author={Ren, Yazhou and Pu, Jingyu and Yang, Zhimeng and Xu, Jie and Li, Guofeng and Pu, Xiaorong and Yu, Philip S. and He, Lifang},
  journal={IEEE Transactions on Neural Networks and Learning Systems}, 
  title={Deep Clustering: A Comprehensive Survey}, 
  year={2025},
  volume={36},
  number={4},
  pages={5858-5878},
  keywords={Feature extraction;Task analysis;Clustering methods;Surveys;Representation learning;Transfer learning;Probability distribution;Deep clustering (DC);multiview clustering (MVC);semi-supervised clustering;transfer learning},
  doi={10.1109/TNNLS.2024.3403155}}

@misc{an-introduction-tp-autoencoders,
      title={An Introduction to Autoencoders}, 
      author={Umberto Michelucci},
      year={2022},
      eprint={2201.03898},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2201.03898}, 
}



@article{tan,
author = {Tan, Pang-Ning and Kumar, Vipin},
year = {2002},
month = {01},
pages = {9-35},
title = {Discovery of Web Robot Sessions Based on Their Navigational Patterns},
volume = {6},
isbn = {978-3-642-07378-6},
journal = {Data Min. Knowl. Discov.},
doi = {10.1023/A:1013228602957}
}

@inproceedings{real-time-crawler-detection,
  author={Balla, Andoena and Stassopoulou, Athena and Dikaiakos, Marios D.},
  booktitle={2011 18th International Conference on Telecommunications}, 
  title={Real-time web crawler detection}, 
  year={2011},
  volume={},
  number={},
  pages={428-432},
  keywords={Crawlers;IP networks;Humans;Real time systems;Robots;Feature extraction;Measurement},
  doi={10.1109/CTS.2011.5898963}}



@inproceedings{fp-crawlers,
  TITLE = {{FP-Crawlers: Studying the Resilience of Browser Fingerprinting to Block Crawlers}},
  AUTHOR = {Vastel, Antoine and Rudametkin, Walter and Rouvoy, Romain and Blanc, Xavier},
  URL = {https://inria.hal.science/hal-02441653},
  BOOKTITLE = {{MADWeb'20 - NDSS Workshop on Measurements, Attacks, and Defenses for the Web}},
  ADDRESS = {San Diego, United States},
  EDITOR = {Oleksii Starov and Alexandros Kapravelos and Nick Nikiforakis},
  YEAR = {2020},
  MONTH = Feb,
  DOI = {10.14722/ndss.2020.23xxx},
  KEYWORDS = {Detection of bots ; browser fingerprinting},
  PDF = {https://inria.hal.science/hal-02441653v1/file/vastel-madweb20.pdf},
  HAL_ID = {hal-02441653},
  HAL_VERSION = {v1},
}



@article{a-survey-of-browser-fingerprint-research-and-application,
author = {Zhang, Desheng and Zhang, Jianhui and Bu, Youjun and Chen, Bo and Sun, Chongxin and Wang, Tianyu},
title = {A Survey of Browser Fingerprint Research and Application},
journal = {Wireless Communications and Mobile Computing},
volume = {2022},
number = {1},
pages = {3363335},
doi = {https://doi.org/10.1155/2022/3363335},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1155/2022/3363335},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1155/2022/3363335},
abstract = {With the development of modern browsing, the convenience brought by rich browser features has also produced a large number of features, which are called browser fingerprints. This article surveys the latest research results on browser fingerprinting, hoping to provide a convenient navigation for newcomers to research or apply this technology in the future. This paper first briefly introduces the browser fingerprinting technology itself, then classifies the related research on browsers, and analyzes the development of different research directions of browser fingerprinting in detail. And through the analysis of the existing results, the problems faced by different research directions are pointed out. After that, this paper introduces the application of browser fingerprint technology in detail and discusses the application achievements and technical challenges of this technology. Next, this paper introduces the theoretical tools related to the research of browser fingerprinting technology and introduces the application of different theoretical tools and practical significance. Finally, the research achievements of browser fingerprint recognition are summarized, and the future development trend is pointed out.},
year = {2022}
}



@article{are_cluster_validity_measures_(in)valid,
   title={Are cluster validity measures (in) valid?},
   volume={581},
   issn={0020-0255},
   url={http://dx.doi.org/10.1016/j.ins.2021.10.004},
   doi={10.1016/j.ins.2021.10.004},
   journal={Information Sciences},
   publisher={Elsevier BV},
   author={Gagolewski, Marek and Bartoszuk, Maciej and Cena, Anna},
   year={2021},
   month= {dec}, 
   pages={620--636} 
}

@misc{a-rapid-review-of-clustering-algorithms,
      title={A Rapid Review of Clustering Algorithms}, 
      author={Hui Yin and Amir Aryani and Stephen Petrie and Aishwarya Nambissan and Aland Astudillo and Shengyuan Cao},
      year={2024},
      eprint={2401.07389},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2401.07389}, 
}

@misc{text-clustering-as-classification-with-llms,
      title={Text Clustering as Classification with LLMs}, 
      author={Chen Huang and Guoxiu He},
      year={2025},
      eprint={2410.00927},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2410.00927}, 
}

@InProceedings{beyond-words-a-comparative-analysis-of-llm-embeddings-for-effective-clustering,
author="Keraghel, Imed
and Morbieu, Stanislas
and Nadif, Mohamed",
editor="Miliou, Ioanna
and Piatkowski, Nico
and Papapetrou, Panagiotis",
title="Beyond Words: A Comparative Analysis of LLM Embeddings for Effective Clustering",
booktitle="Advances in Intelligent Data Analysis XXII",
year="2024",
publisher="Springer Nature Switzerland",
address="Cham",
pages="205--216",
abstract="The document clustering process involves the grouping of similar unlabeled textual documents. This task relies on the use of document embedding techniques, which can be derived from various models, including traditional and neural network-based approaches. The emergence of Large Language Models (LLMs) has provided a new method of capturing information from texts through customized numerical representations, potentially enhancing text clustering by identifying subtle semantic connections. The objective of this paper is to demonstrate the impact of LLMs of different sizes on text clustering. To accomplish this, we select five different LLMs and compare them with three less resource-intensive embedding methods. Additionally, we utilize six clustering algorithms. We simultaneously assess the performance of the embedding models and clustering algorithms in terms of clustering quality, and highlight the strengths and limitations of the models under investigation.",
isbn="978-3-031-58547-0"
}


@article{genieclust_fast_and_robust_hierarchical_clustering,
title = {genieclust: Fast and robust hierarchical clustering},
journal = {SoftwareX},
volume = {15},
pages = {100722},
year = {2021},
issn = {2352-7110},
doi = {https://doi.org/10.1016/j.softx.2021.100722},
url = {https://www.sciencedirect.com/science/article/pii/S2352711021000649},
author = {Marek Gagolewski},
keywords = {Hierarchical clustering, Robust methods, Noise points, Python, R},
abstract = {genieclust is an open source Python and R package that implements the hierarchical clustering algorithm called Genie. This method frequently outperforms other state-of-the-art approaches in terms of clustering quality and speed, supports various distances over dense, sparse, and string data domains, and can be robustified even further with the built-in noise point detector. As domain-independent software, it can be used for solving problems arising in all data-driven research and development activities, including environmental, health, biological, physical, decision, and social sciences as well as technology and engineering. The Python version provides a scikit-learn-compliant API, whereas the R variant is compatible with the classic hclust(). Numerous tutorials, use cases, non-trivial examples, documentation, installation instructions, benchmark results and timings can be found at https://genieclust.gagolewski.com/.}
}

@article{to-cluster-or-not-to-cluster-an-analysis-of-clusterability-methods,
   title={To cluster, or not to cluster: An analysis of clusterability methods},
   volume={88},
   ISSN={0031-3203},
   url={http://dx.doi.org/10.1016/j.patcog.2018.10.026},
   DOI={10.1016/j.patcog.2018.10.026},
   journal={Pattern Recognition},
   publisher={Elsevier BV},
   author={Adolfsson, Andreas and Ackerman, Margareta and Brownstein, Naomi C.},
   year={2019},
   month=apr, pages={13-26} }

@article{the-dip-test-of-unimodality,
author = {J. A. Hartigan and P. M. Hartigan},
title = {{The Dip Test of Unimodality}},
volume = {13},
journal = {The Annals of Statistics},
number = {1},
publisher = {Institute of Mathematical Statistics},
pages = {70 -- 84},
keywords = {Empirical distribution, isotonic regression, multimodality},
year = {1985},
doi = {10.1214/aos/1176346577},
URL = {https://doi.org/10.1214/aos/1176346577}
}

@article{from_a-to-z_review_of_clustering_validation_indices,
   title={From A-to-Z review of clustering validation indices},
   volume={601},
   ISSN={0925-2312},
   url={http://dx.doi.org/10.1016/j.neucom.2024.128198},
   DOI={10.1016/j.neucom.2024.128198},
   journal={Neurocomputing},
   publisher={Elsevier BV},
   author={Hassan, Bryar A. and Tayfor, Noor Bahjat and Hassan, Alla A. and Ahmed, Aram M. and Rashid, Tarik A. and Abdalla, Naz N.},
   year={2024},
   month=oct, pages={128198} 
}

@article{wb-index-a-sum-of-squares-based-index-for-cluster-validity,
title = {WB-index: A sum-of-squares based index for cluster validity},
journal = {Data \& Knowledge Engineering},
volume = {92},
pages = {77-89},
year = {2014},
issn = {0169-023X},
doi = {10.1016/j.datak.2014.07.008},
url = {https://www.sciencedirect.com/science/article/pii/S0169023X14000676},
author = {Qinpei Zhao and Pasi Fränti},
keywords = {Cluster validity, Keywords categorization, Short text mining, Clustering, Classification and association rules},
abstract = {Determining the number of clusters is an important part of cluster validity that has been widely studied in cluster analysis. Sum-of-squares based indices show promising properties in terms of determining the number of clusters. However, knee point detection is often required because most indices show monotonicity with increasing number of clusters. Therefore, indices with a clear minimum or maximum value are preferred. The aim of this paper is to revisit a sum-of-squares based index called the WB-index that has a minimum value as the determined number of clusters. We shed light on the relation between the WB-index and two popular indices which are the Calinski–Harabasz and the Xu-index. According to a theoretical comparison, the Calinski–Harabasz index is shown to be affected by the data size and level of data overlap. The Xu-index is close to the WB-index theoretically, however, it does not work well when the dimension of the data is greater than two. Here, we conduct a more thorough comparison of 12 internal indices and provide a summary of the experimental performance of different indices. Furthermore, we introduce the sum-of-squares based indices into automatic keyword categorization, where the indices are specially defined for determining the number of clusters.}
}

@article{silhouettes_a_graphical_aid_to_the_interpretation_and_validation_of_cluster_analysis,
title = {Silhouettes: A graphical aid to the interpretation and validation of cluster analysis},
journal = {Journal of Computational and Applied Mathematics},
volume = {20},
pages = {53-65},
year = {1987},
issn = {0377-0427},
doi = {https://doi.org/10.1016/0377-0427(87)90125-7},
url = {https://www.sciencedirect.com/science/article/pii/0377042787901257},
author = {Peter J. Rousseeuw},
keywords = {Graphical display, cluster analysis, clustering validity, classification},
abstract = {A new graphical display is proposed for partitioning techniques. Each cluster is represented by a so-called silhouette, which is based on the comparison of its tightness and separation. This silhouette shows which objects lie well within their cluster, and which ones are merely somewhere in between clusters. The entire clustering is displayed by combining the silhouettes into a single plot, allowing an appreciation of the relative quality of the clusters and an overview of the data configuration. The average silhouette width provides an evaluation of clustering validity, and might be used to select an ‘appropriate’ number of clusters.}
}

@ARTICLE{a_cluster_separation_measure,
  author={Davies, David L. and Bouldin, Donald W.},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, 
  title={A Cluster Separation Measure}, 
  year={1979},
  volume={PAMI-1},
  number={2},
  pages={224-227},
  keywords={Dispersion;Density measurement;Algorithm design and analysis;Clustering algorithms;Partitioning algorithms;Multidimensional systems;Data analysis;Performance analysis;Humans;Missiles;Cluster;data partitions;multidimensional data analysis;parametric clustering;partitions;similarity measure},
  doi={10.1109/TPAMI.1979.4766909}}

@inbook{density_based_clustering_validation,
author = {Davoud Moulavi and Pablo A. Jaskowiak and Ricardo J. G. B. Campello and Arthur Zimek and Jörg Sander},
title = {Density-Based Clustering Validation},
booktitle = {Proceedings of the 2014 SIAM International Conference on Data Mining (SDM)},
chapter = {},
pages = {839-847},
doi = {10.1137/1.9781611973440.96},
URL = {https://epubs.siam.org/doi/abs/10.1137/1.9781611973440.96},
eprint = {https://epubs.siam.org/doi/pdf/10.1137/1.9781611973440.96},
    abstract = { Abstract One of the most challenging aspects of clustering is validation, which is the objective and quantitative assessment of clustering results. A number of different relative validity criteria have been proposed for the validation of globular, clusters. Not all data, however, are composed of globular clusters. Density-based clustering algorithms seek partitions with high density areas of points (clusters, not necessarily globular) separated by low density areas, possibly containing noise objects. In these cases relative validity indices proposed for globular cluster validation may fail. In this paper we propose a relative validation index for density-based, arbitrarily shaped clusters. The index assesses clustering quality based on the relative density connection between pairs of objects. Our index is formulated on the basis of a new kernel density function, which is used to compute the density of objects and to evaluate the within- and between-cluster density connectedness of clustering results. Experiments on synthetic and real world data show the effectiveness of our approach for the evaluation and selection of clustering algorithms and their respective appropriate parameters. }
}

@ARTICLE{the-self-organizing-map,
  author={Kohonen, T.},
  journal={Proceedings of the IEEE}, 
  title={The self-organizing map}, 
  year={1990},
  volume={78},
  number={9},
  pages={1464-1480},
  keywords={Biological neural networks;Artificial neural networks;Pattern recognition;Process control;Signal processing;Computer networks;Signal processing algorithms;Animals;Organizing;Speech recognition},
  doi={10.1109/5.58325}}

@article{essential-of-the-self-organizing-map,
title = {Essentials of the self-organizing map},
journal = {Neural Networks},
volume = {37},
pages = {52-65},
year = {2013},
note = {Twenty-fifth Anniversay Commemorative Issue},
issn = {0893-6080},
doi = {https://doi.org/10.1016/j.neunet.2012.09.018},
url = {https://www.sciencedirect.com/science/article/pii/S0893608012002596},
author = {Teuvo Kohonen},
keywords = {Self-organizing map, SOM, Data analysis, Brain map, Similarity, Vector quantization},
abstract = {The self-organizing map (SOM) is an automatic data-analysis method. It is widely applied to clustering problems and data exploration in industry, finance, natural sciences, and linguistics. The most extensive applications, exemplified in this paper, can be found in the management of massive textual databases and in bioinformatics. The SOM is related to the classical vector quantization (VQ), which is used extensively in digital signal processing and transmission. Like in VQ, the SOM represents a distribution of input data items using a finite set of models. In the SOM, however, these models are automatically associated with the nodes of a regular (usually two-dimensional) grid in an orderly fashion such that more similar models become automatically associated with nodes that are adjacent in the grid, whereas less similar models are situated farther away from each other in the grid. This organization, a kind of similarity diagram of the models, makes it possible to obtain an insight into the topographic relationships of data, especially of high-dimensional data items. If the data items belong to certain predetermined classes, the models (and the nodes) can be calibrated according to these classes. An unknown input item is then classified according to that node, the model of which is most similar with it in some metric used in the construction of the SOM. A new finding introduced in this paper is that an input item can even more accurately be represented by a linear mixture of a few best-matching models. This becomes possible by a least-squares fitting procedure where the coefficients in the linear mixture of models are constrained to nonnegative values.}
}

@article{survey-and-comparison-of-quality-measures-for-self-organizing-maps,
author = {Polzlbauer, Georg},
year = {2004},
month = {01},
pages = {},
title = {Survey and comparison of quality measures for self-organizing maps},
journal = {Proceedings of the Fifth Workshop on Data Analysis (WDA04)}
}

@misc{a-survey-and-implementation-of-performance-metrics-for-self-organized-maps,
      title={A Survey and Implementation of Performance Metrics for Self-Organized Maps}, 
      author={Florent Forest and Mustapha Lebbah and Hanane Azzag and Jérôme Lacaille},
      year={2020},
      eprint={2011.05847},
      archivePrefix={arXiv},
      primaryClass={cs.NE},
      url={https://arxiv.org/abs/2011.05847}, 
}

@article{clustering-of-the-self-organizing-map,
  author={Vesanto, J. and Alhoniemi, E.},
  journal={IEEE Transactions on Neural Networks}, 
  title={Clustering of the self-organizing map}, 
  year={2000},
  volume={11},
  number={3},
  pages={586-600},
  keywords={Prototypes;Data visualization;Data mining;Data analysis;Data acquisition;Data preprocessing;Topology;Neural networks;Electronic mail;Clustering methods},
  doi={10.1109/72.846731}}

@article{on-the-use-of-self-organizing-map-for-text-clustering-in-engineering-change-process-analysis-a-case-study,
author = {Pacella, Massimo and Blaco, Marzia},
year = {2016},
month = {01},
pages = {1-11},
title = {On the Use of Self-Organizing Map for Text Clustering in Engineering Change Process Analysis: A Case Study},
volume = {2016},
journal = {Computational Intelligence and Neuroscience},
doi = {10.1155/2016/5139574}
}

@misc{minisom,
  title={MiniSom: minimalistic and NumPy-based implementation of the Self Organizing Map},
  author={Giuseppe Vettigli},
  year={2018},
  url={https://github.com/JustGlowing/minisom/},
}
