Unsupervised Learning Algorithm Research Articles

Machine learning-assisted rapid determination for traditional Chinese Medicine Constitution

The aim of this study was to develop a machine learning-assisted rapid determination methodology for traditional Chinese Medicine Constitution. Based on the Constitution in Chinese Medicine Questionnaire (CCMQ), the most applied diagnostic instrument for assessing individuals’ constitutions, we employed automated supervised machine learning algorithms (i.e., Tree-based Pipeline Optimization Tool; TPOT) on all the possible item combinations for each subscale and an unsupervised machine learning algorithm (i.e., variable clustering; varclus) on the whole scale to select items that can best predict body constitution (BC) classifications or BC scores. By utilizing subsets of items selected based on TPOT and corresponding machine learning algorithms, the accuracies of BC classifications prediction ranged from 0.819 to 0.936, with the root mean square errors of BC scores prediction stabilizing between 6.241 and 9.877. Overall, the results suggested that the automated machine learning algorithms performed better than the varclus algorithm for item selection. Additionally, based on an automated machine learning item selection procedure, we provided the top three ranked item combinations with each possible subscale length, along with their corresponding algorithms for predicting BC classification and severity. This approach could accommodate the needs of different practitioners in traditional Chinese medicine for rapid constitution determination.

Research On Purchasing Behavior and Marketing Strategy Optimization of Commercial Medical Insurance Based on Unsupervised Learning Algorithms

Being a marketer in today's world is not an easy job, especially in the insurance industry, where many factors affect whether customers buy insurance and how much they buy. This field also has many research topics and areas worth exploring. In this article, the data used is real insurance sales data and used unsupervised learning methods in machine learning to clean, mine and analyze the data. First, using clustering methods to divide people who buy insurance into three categories based on weight and insurance expenses. Then using association rule learning methods to analyze the recurring features among people who buy insurance. Finally, based on these results, many policies and suggestions can be given from the perspective of insurance companies and sellers.

Using machine learning to gain insights into chronic disease multimorbidity: trends and patterns in British Columbia, Canada

ObjectiveThe goal of this project is to explore novel ways to assess chronic disease multimorbidity (co-occurrence of two or more conditions) trends and patterns in the population of British Columbia (BC), Canada. ApproachThis study included linked data from the BC population (~5M individuals) from 2001/02 to 2019/20. We analysed 25 chronic conditions, including 7 primary cancer subtypes. We report multimorbidity (MM) incidence, prevalence, and most common disease combinations. Further we explore temporal MM disease patterns using directed network analyses and extracted data-driven disease clusters with an unsupervised machine learning algorithm. ResultsThe age-standardized incidence of MM stayed relatively stable over the study period for males and decreased for females, while prevalence increased to approximately 1 in 4 individuals in BC in 2019/20 (from 19% to 27% of females, from 15% to 22% of males). Disease networks and clusters varied significantly by sex and age group, this presentation will highlight select disease network and cluster findings and discuss their implications for chronic disease surveillance. ConclusionsThe prevalence of multimorbidity continues to rise in BC. Using advanced analytics to understand disease co-occurrence patterns provides new insights above and beyond traditional epidemiological metrics to support health system planning and prevention efforts. ImplicationsChronic disease surveillance and research have historically operated with a single disease focus, which is not patient-centered and does not adequately account for the reality of multimorbidity for many people. This project is laying the foundation for enhanced chronic disease surveillance and monitoring in BC.

The degenerate coding of psychometric profiles through functional connectivity archetypes

IntroductionDegeneracy in the brain-behavior code refers to the brain’s ability to utilize different neural configurations to support similar functions, reflecting its adaptability and robustness. This study aims to explore degeneracy by investigating the non-linear associations between psychometric profiles and resting-state functional connectivity (RSFC).MethodsThe study analyzed RSFC data from 500 subjects to uncover the underlying neural configurations associated with various psychometric outcomes. Self-organized maps (SOM), a type of unsupervised machine learning algorithm, were employed to cluster the RSFC data. And identify distinct archetypal connectivity profiles characterized by unique within- and between-network connectivity patterns.ResultsThe clustering analysis using SOM revealed several distinct archetypal connectivity profiles within the RSFC data. Each archetype exhibited unique connectivity patterns that correlated with various cognitive, physical, and socioemotional outcomes. Notably, the interaction between different SOM dimensions was significantly associated with specific psychometric profiles.DiscussionThis study underscores the complexity of brain-behavior interactions and the brain’s capacity for degeneracy, where different neural configurations can lead to similar behavioral outcomes. These findings highlight the existence of multiple brain architectures capable of producing similar behavioral outcomes, illustrating the concept of neural degeneracy, and advance our understanding of neural degeneracy and its implications for cognitive and emotional health.

Feature Selection and Dimensionality Reduction for Large Data set Clustering using Self-Organizing Maps

Data mining is a form of analyzing the data which relates the newline techniques from fields like statistics machine learning databases artificial newline intelligence etc Clustering is one of the most important data mining newline techniques in which cluster of objects are grouped based on their similarity newline "Clustering is the process of accumulating the data records into considerable newline subclasses clusters in a way which enhances the relationship within clusters newline and reduces the similarity among two different clusters. The activity occurs every new academic year and schools with plenty of new students registered may feel a bit overwhelmed with this grouping assignment. A decision support system which can automatically perform grouping on a list of students may be able to help the school’s staffs with this repetitive task. A self-organizing map (SOM) is an example of unsupervised learning algorithm using an artificial neural network structure to produce a low dimensional representation from a given input.

Variability of morphology in photoplethysmographic waveform quantified with unsupervised wave-shape manifold learning for clinical assessment

Objective.We investigated fluctuations of the photoplethysmography (PPG) waveform in patients undergoing surgery. There is an association between the morphologic variation extracted from arterial blood pressure (ABP) signals and short-term surgical outcomes. The underlying physiology could be the numerous regulatory mechanisms on the cardiovascular system. We hypothesized that similar information might exist in PPG waveform. However, due to the principles of light absorption, the noninvasive PPG signals are more susceptible to artifacts and necessitate meticulous signal processing.Approach.Employing the unsupervised manifold learning algorithm, dynamic diffusion map, we quantified multivariate waveform morphological variations from the PPG continuous waveform signal. Additionally, we developed several data analysis techniques to mitigate PPG signal artifacts to enhance performance and subsequently validated them using real-life clinical database.Main results.Our findings show similar associations between PPG waveform during surgery and short-term surgical outcomes, consistent with the observations from ABP waveform analysis.Significance.The variation of morphology information in the PPG waveform signal in major surgery provides clinical meanings, which may offer new opportunity of PPG waveform in a wider range of biomedical applications, due to its non-invasive nature.

On particle dispersion statistics using unsupervised learning and Gaussian mixture models

Understanding the dispersion of particles in enclosed spaces is crucial for controlling the spread of infectious diseases. This study introduces an innovative approach that combines an unsupervised learning algorithm with a Gaussian mixture model to analyze the behavior of saliva droplets emitted from a coughing individual. The algorithm effectively clusters data, while the Gaussian mixture model captures the distribution of these clusters, revealing underlying sub-populations and variations in particle dispersion. Using computational fluid dynamics simulation data, this integrated method offers a robust, data-driven perspective on particle dynamics, unveiling intricate patterns and probabilistic distributions previously unattainable. The combined approach significantly enhances the accuracy and interpretability of predictions, providing valuable insights for public health strategies to prevent virus transmission in indoor environments. The practical implications of this study are profound, as it demonstrates the potential of advanced unsupervised learning techniques in addressing complex biomedical and engineering challenges and underscores the importance of coupling sophisticated algorithms with statistical models for comprehensive data analysis. The potential impact of these findings on public health strategies is significant, highlighting the relevance of this research to real-world applications.

Achieving the United Nations sustainable development goals – innovation diffusion and business model innovations

Purpose The study aims to analyse the race towards green development and United Nations sustainable development goals (SDGs) in the cases of Huawei and Shell. Both companies are the leaders in their respective industries. Huawei is an example case study representing the information and communications technology (ICT) industry whereas Shell is an example case study representing the oil and gas industry. The research analyses of the races in achieving UN SDGs were undertaken based on the innovation diffusion framework with the use of machine learning algorithms trained to extract data on sustainability activities and initiatives. Design/methodology/approach The research analyses the two case studies of Huawei and Shell. The research was undertaken through the steps of training machine learning algorithms, industry benchmarking and evaluating the performance of the race. The analyses regarding the activities and initiatives of Huawei and Shell in contributing towards SDGs are based on the data in the past 10 years (Years 2010–2019) using machine learning to extract data on sustainability activities and initiatives. In the case of Huawei, 313 sustainability reports were fed to the unsupervised machine learning algorithms revealing 15,101 sustainability actions and initiatives related to UN SDGs in the ICT industry. In the case of Shell, 2,015 sustainability reports were fed to the unsupervised machine learning algorithms revealing 47,365 sustainability actions and initiatives related to UN SDGs in the oil and gas industry. Findings The analyses of findings revealed that Huawei and Shell performed very well in progressing towards the UN SDGs. Huawei had strong performance in the ICT industry with regard to SDGs No. 3, 4, 7, 8, 11, 12 and 16 while Shell had strong performance in the oil and gas industry with regard to SDGs No. 3, 4, 6, 7, 8, 12 and 16. Both companies had placed a focus on achieving SDG 12 responsible consumption and production, SDG 7 affordable and clean energy and SDG 4 quality education. The synthesised business model innovations of Huawei and Shell had shown their environmental, social and governance strategies – Huawei’s 2030 vision for green development and Shell’s 2050 vision for net zero emissions. Practical implications The five pillars of people, planet, prosperity, peace and partnership according to the UN 2030 agenda for sustainable development have shown the way a company operates to promote sustainable eco-systems. The extent to which both Huawei and Shell link corporate strategies to the UN SDGs has reflected their implementation progress. Furthermore, the business model innovations of Huawei and Shell provides a useful framework which can be applied to encourage other companies/organisations in various industries to undertake ESG activities in practice. Originality/value The main contribution of this research is the application of machine learning algorithms and the innovation diffusion model in analysing the SDGs performance. The study applies the innovation diffusion framework to explore strategic actions and initiatives of Huawei and Shell in transitioning towards sustainability. The use of machine learning algorithms has identified their sustainability approach in achieving the UN SDGs.

Software defined networking based network traffic classification using machine learning techniques

The classification of network traffic has become increasingly crucial due to the rapid growth in the number of internet users. Conventional approaches, such as identifying traffic based on port numbers and payload inspection are becoming ineffective due to the dynamic and encrypted nature of modern network traffic. A number of researchers have implemented Software Defined Networking (SDN) based traffic classification using Machine Learning (ML) and Deep Learning (DL) models. However, the studies had various limitations such as encrypted traffic detection, payload inspection, poor detection accuracy, and challenges with testing models both in offline and real-time traffic modes. ML models together with SDN are adopted nowadays to enhance classification performance. In this paper, both supervised (Logistic Regression, Decision Tree, Random Forest, AdaBoost, and Support Vector Machine) and unsupervised (K-means clustering) ML models were used to classify Domain Name System (DNS), Telnet, Ping, and Voice traffic flows simulated using the Distributed Internet Traffic Generator (D-ITG) tool. The use of this tool effectively manages and classifies traffic types based on their application. The study discussed the dataset used, model selection, implementation of the model, and implementation techniques (such as pre-processing, feature extraction, ML algorithm, and model evaluation metrics). The proposed model in SDN was implemented in Mininet for designing the network architecture and generating network traffic. Anaconda Python environment was utilized for traffic classification using various ML techniques. Among the models tested, the Decision Tree supervised learning achieved the highest accuracy of 99.81%, outperforming other supervised and unsupervised learning algorithms. These results indicate that the integration of ML with SDN provides an efficient classification method for identifying and accurately classifying both offline and real-time network traffic, enhanced quality of service (QoS), detection of encrypted packets, deep packet inspection and management.

Fault diagnosis of satellite power system based on unsupervised knowledge acquisition and decision-making

Fault diagnosis (FD) is an important foundation for the maintenance of complex aerospace systems, such as satellite power systems, in which the attribute reduction has essential effect to eliminating data redundancy and improving diagnostic results. However, due to the difficulty and high cost of obtaining labels in some situations, especially early failures, FD based on unsupervised methods is of great significance but less commonly-studied. Moreover, with respect to FD preprocessing, unsupervised attribute reduction (UAR) usually applying clustering methods suffers from the need for cluster number, randomness, inability to handle non-spherical clusters, etc. Therefore, this paper proposes an unsupervised FD strategy including a knowledge acquisition method to mine the rules from the unlabeled data, a decision-making method to process the acquired knowledge, and a diagnosis decision for the fault identification. As for the preprocessing part, this paper proposes a wrapper UAR method (named DPC-UAR) based on the density peak clustering (DPC) and heuristic method, which can automatically identify the cluster centers and deal with the nonspherical data. Finally, experiments of attribute reduction performance on UCI data show that compared with other UAR methods, DPC-UAR has the greatest effect to improve performance of unsupervised learning algorithms, and plays a relatively good role in the supervised algorithm. Experiments on satellite power system fault diagnosis illustrated that the proposed FD strategy based on DPC-UAR has high accuracy, a high fault detection rate, and a low false alarm rate.

Anomaly detection for atomic clocks using unsupervised machine learning algorithms

Atomic clocks are the most accurate timekeeping instruments in the world and play a crucial role in numerous applications, notably in satellite navigation. Nonetheless, the accuracy of these clock signals is subject to anomalies, highlighting the importance of anomaly characterization and detection. In our study, we identify three types of anomalies in the phase data of atomic clocks—outliers, phase jumps and inflection points, and also frequency jumps in the frequency data, which corresponds to the inflection points. The novelty of our work lies in the application of machine learning methods for atomic clock anomaly detection (AD), as opposed to traditional algorithms. We introduce a variety of unsupervised AD and change point detection (CPD) algorithms from the realms of statistics and machine learning, propose a customized thresholding function for AD, and evaluate AD and CPD algorithms on both synthetic and real-world atomic clock data. Our experiments reveal that the subsequence local outlier factor algorithm achieves excellent performance in detecting outliers and phase jumps, but struggles with inflection points. Crucially, we found that the application of a CPD algorithm specifically for detecting the frequency jumps in frequency data substantially improves performance. Our study adopts unsupervised machine learning methods for AD provides robust detection strategies, thereby contributing an expanded understanding of the use of machine learning for clock data monitoring.

Research on Using K-Means Clustering to Explore High-Risk Products with Ethylene Oxide Residues and Their Manufacturers in Taiwan.

Considering the frequency of ethylene oxide (EtO) residues found in food, the health effects of EtO have become a concern. Between 2022 and 2023, 489 products were inspected using the purposive sampling method in Taiwan, and nine unqualified products were found to have been imported; subsequently, border control measures were enhanced. To ensure the safety of all imported foods, the current study used the K-means clustering method for identifying EtO residues in food. Data on finished products and raw materials with EtO residues from international public opinion bulletins were collected for analysis. After matching them with the Taiwan Food Cloud, 90 high-risk food items with EtO residues and 1388 manufacturers were screened. The Taiwan Food and Drug Administration set up border controls and grouped the manufacturers using K-means clustering in the unsupervised learning algorithm. For this study, 37 manufacturers with priority inspections and 52 high-risk finished products and raw materials with residual EtO were selected for inspection. While EtO was not detected, the study concluded the following: 1. Using international food safety alerts to strengthen border control can effectively ensure domestic food safety; 2. K-means clustering can validate the risk-based purposive sampling results to ensure food safety and reduce costs.

A Cyborg Walk for Urban Analysis? From Existing Walking Methodologies to the Integration of Machine Learning

Although walking methodologies (WMs) and machine learning (ML) have been objects of interest for urban scholars, it is difficult to find research that integrates both. We propose a ‘cyborg walk’ method and apply it to studying litter in public spaces. Walking routes are created based on an unsupervised learning algorithm (k-means) to classify public spaces. Then, a deep learning model (YOLOv5) is used to collect data from geotagged photos taken by an automatic Insta360 X3 camera worn by human walkers. Results from image recognition have an accuracy between 83.7% and 95%, which is similar to what is validated by the literature. The data collected by the machine are automatically georeferenced thanks to the metadata generated by a GPS attached to the camera. WMs could benefit from the introduction of ML for informative route optimisation and georeferenced visual data quantification. The links between these findings and the existing WM literature are discussed, reflecting on the parallels between this ‘cyborg walk’ experiment and the seminal cyborg metaphor proposed by Donna Haraway.

Quantum support vector data description for anomaly detection

Abstract Anomaly detection is a critical problem in data analysis and pattern recognition, finding applications in various domains. We introduce quantum support vector data description (QSVDD), an unsupervised learning algorithm designed for anomaly detection. QSVDD utilizes a shallow-depth quantum circuit to learn a minimum-volume hypersphere that tightly encloses normal data, tailored for the constraints of noisy intermediate-scale quantum (NISQ) computing. Simulation results on the MNIST and Fashion MNIST image datasets, as well as credit card fraud detection, demonstrate that QSVDD outperforms both quantum autoencoder and deep learning-based approaches under similar training conditions. Notably, QSVDD requires an extremely small number of model parameters, which increases logarithmically with the number of input qubits. This enables efficient learning with a simple training landscape, presenting a compact quantum machine learning model with strong performance for anomaly detection.

A hybrid unsupervised clustering method for predicting the risk of dental implant loss

ObjectivesThe aim of this study was to predict the risk of dental implant loss by clustering features associated with implant survival rates. Materials and methodsMultiple clinical features from 8513 patients who underwent single implant placement were retrospectively analysed. A hybrid method integrating unsupervised learning algorithms with survival analysis was employed for data mining. Two-step cluster, univariate Cox regression, and Kaplan‒Meier survival analyses were performed to identify the clustering features associated with implant survival rates. To predict the risk of dental implant loss, nomograms were constructed on the basis of time-stratified multivariate Cox regression. ResultsSix clusters with distinct features and prognoses were identified using two-step cluster analysis and Kaplan‒Meier survival analysis. Compared with the other clusters, only one cluster presented significantly lower implant survival rates, and six specific clustering features within this cluster were identified as high-risk factors, including age, smoking history, implant diameter, implant length, implant position, and surgical procedure. Nomograms were created to assess the impact of the six high-risk factors on implant loss for three periods: 1) 0–120 days, 2) 120–310 days, and 3) more than 310 days after implant placement. The concordance indices of the models were 0.642, 0.781, and 0.715, respectively. ConclusionsThe hybrid unsupervised clustering method, which clusters and identifies high-risk clinical features associated with implant loss without relying on predefined labels or target variables, represents an effective approach for developing a visual model for predicting implant prognosis. However, further validation with a multimodal, multicentre, prospective cohort is needed. Clinical significanceVisual prognosis prediction utilizing this nomogram that predicts the risk of implant loss on the basis of clustering features can assist dentists in preoperative assessments and clinical decision-making, potentially improving dental implant prognosis.

Geologically constrained unsupervised dual-branch deep learning algorithm for geochemical anomalies identification

The identification of geochemical anomalies is crucial in mineral exploration. However, the limited sample size, high-dimensional features, and mixed geochemical information make identifying geochemical anomalies a significant challenge. Machine learning algorithms (MLAs), especially those with spatial and spectrum branches, have been proven to be a high efficiency tools for detecting geochemical anomalies related to mineralization. The spatial branch MLAs take two-dimensional images (pixel-patches) as input and mainly capture the spatial characteristics of geochemical patterns. The spectrum branch MLAs take one-dimensional sequence data (pixels) as input and mainly consider the elemental concentration and assemblies. Simultaneously considering the spatial patterns and geochemical concentrations of the geochemical survey data can mitigate geochemical concentration variations arising from objective factors and amplify subtle mineralization anomalies. This study proposes an unsupervised spatial-spectrum autoencoder (AE), namely dual-AE, which consists of a graph convolutional autoencoder (GCN-AE) and a long short-term memory network autoencoder (LSTM-AE) for geochemical anomalies identification. The spatial branch is constructed using the GCN-AE, which can effectively capture spatial geochemical patterns and extract spatial relationships between neighboring pixels. The spectrum branch consists of an LSTM-AE that can study geochemical elemental assemblies within a single pixel. A key ore-controlling factor was added into the dual-AE as a soft constraint to construct a geologically constrained dual-AE. A case study was conducted to recognize geochemical anomalies associated with iron polymetallic mineralization in Southwest Fujian Province, China. The obtained results demonstrated that (1) the unsupervised spatial-spectrum deep learning algorithm serves as a potent method for detecting geochemical anomalies related to mineralization, (2) the geologically constrained unsupervised spatial-spectrum dual-branch model can improve the accuracy and interpretability of geochemical anomaly identification, and (3) the identified anomalous areas can provide essential clues for further mineral exploration.

Towards Unsupervised Learning Driven Intelligence for Prediction of Prostate Cancer

Prostate cancer is a widespread and global disease which affects adult males – it is said that key causes of the cancer include age, family history and ethnicity. In this study, the Kaggle prostate cancer dataset, comprising of data of 100 patients with a mixture that both had cancer and did not have cancer, was used alongside machine learning prediction models for the design of unsupervised and automated intelligent systems for the prediction of prostate cancer. Two intelligent systems were designed and underpinned by unsupervised learning algorithms, namely, fuzzy c-means and agglomerative hierarchical clustering, where the various intelligent systems were able to make a prostate cancer prediction with accuracies of over 80% for the various classification metrics, alongside being able to predict an associated stage of the prostate cancer. Both designed intelligent systems offer a complimentary alternative to each other, and their relative merits are discussed in the paper. ry alternative to each other, and their relative merits are discussed in the paper.

Image reconstruction for compressed ultrafast photography based on manifold learning and the alternating direction method of multipliers

Compressed ultrafast photography (CUP) is a high-speed imaging technique with a frame rate of up to ten trillion frames per second (fps) and a sequence depth of hundreds of frames. This technique is a powerful tool for investigating ultrafast processes. However, since the reconstruction process is an ill-posed problem, the image reconstruction will be more difficult with the increase of the number of reconstruction frames and the number of pixels of each reconstruction frame. Recently, various deep-learning-based regularization terms have been used to improve the reconstruction quality of CUP, but most of them require extensive training and are not generalizable. In this paper, we propose a reconstruction algorithm for CUP based on the manifold learning and the alternating direction method of multipliers framework (ML-ADMM), which is an unsupervised learning algorithm. This algorithm improves the reconstruction stability and quality by initializing the iterative process with manifold modeling in embedded space (MMES) and processing the image obtained from each ADMM iterative with a nonlinear modeling based on manifold learning. The numerical simulation and experiment results indicate that most of the spatial details can be recovered and local noise can be eliminated. In addition, a high-spatiotemporal-resolution video sequence can be acquired. Therefore, this method can be applied for CUP with ultrafast imaging applications in the future.

Predictive model for novel subtypes of patients undergoing lower extremity amputation for peripheral artery disease: An unsupervised machine learning study

BackgroundPeripheral artery disease (PAD) represents the frequently seen circulatory condition related to a risk of critical limb ischemia and amputation. Critical lower extremity ischemia may require amputation, and the outcomes vary. In this study, we developed an artificial intelligence (AI)-driven predictive model for PAD subtypes to assess risk among patients more precisely and accurately to predict disease progression. MethodsThe present retrospective study examined clinical data in PAD patents undergoing lower extremity amputation. The data were analyzed using an unsupervised machine learning algorithm (UMLA) for subgroup identification and risk stratification. The clustering result accuracy was validated by analyzing the follow-up data of clusters. Finally, we built the prediction model with binary logistic regression. ResultsIn total, we enrolled 507 cases into this work. Two distinct subgroups, consisting of Clusters 1 and 2, were identified by UMLA; those from Cluster 1 showed markedly poorer conditions and prognostic outcomes compared with those from Cluster 2. With regard to the new PAD subtype, we established a nomogram with eight predictive factors, including gender, age, smoking history, diabetes and coronary heart disease history, albumin levels, endovascular intervention, and amputation level. The nomogram could accurately categorize patients into two identified clusters, and the area under receiver operating characteristic curve was 0.861 (95 % confidence interval: 0.830–0.893). ConclusionIn this study, UMLA was used to identify new phenotypic subgroups among PAD cases who showed different risks of amputation. Our constructed AI-driven predictive model for PAD subtypes showed that it can be used for risk stratification and clinical management with high accuracy and reliability.

Clustering Methods for the Characterization of Synchrotron Radiation X‐Ray Fluorescence Images of Human Carotid Atherosclerotic Plaque

This study employs computational algorithms to automatically identify and classify features in X‐Ray fluorescence (XRF) microscopy images. Principal component analysis (PCA) and unsupervised machine learning algorithms, such as Gaussian mixture (GM) clustering, are implemented to label features on a collection of XRF maps of human atherosclerotic plaque samples. The investigation involves the hard X‐Ray nanoprobe (NanoMAX) at MAX IV synchrotron radiation facility, utilizing scanning transmission X‐Ray microscopy (STXM) and XRF techniques. The analysis covers regions of interest scanned by the beam with a step size of 200 nm, yielding XRF maps of elements like calcium, iron, and zinc. These maps reveal intricate structures unsuitable for manual labeling. However, they can be accurately classified in an automated fashion using GM. Prior to clustering, PCA is used to deal with repeated patterns and background areas. The resulting clusters are associated with different types of features, which can be identified as specific tissues confirmed by histology. Regions of high concentrations of phosphorus, sulfur, calcium, and iron are found in the samples. These regions are also observed in the STXM results as spots of low transmission that typically are associated with calcium deposits only.