Toward smart tire management: A data-driven unsupervised learning approach

A data-driven approach for clustering extra high voltage buses: A case study on the Italian transmission network

While machine learning and artificial intelligence have long been applied in networking research, the bulk of such works has focused on supervised learning. Recently there has been a rising trend of employing unsupervised machine learning using unstructured raw network data to improve network performance and provide services such as traffic engineering, anomaly detection, Internet traffic classification, and quality of service optimization. The interest in applying unsupervised learning techniques in networking emerges from their great success in other fields such as computer vision, natural language processing, speech recognition, and optimal control (e.g., for developing autonomous self-driving cars). Unsupervised learning is interesting since it can unconstrain us from the need of labeled data and manual handcrafted feature engineering thereby facilitating flexible, general, and automated methods of machine learning. The focus of this survey paper is to provide an overview of the applications of unsupervised learning in the domain of networking. We provide a comprehensive survey highlighting the recent advancements in unsupervised learning techniques and describe their applications for various learning tasks in the context of networking. We also provide a discussion on future directions and open research issues, while also identifying potential pitfalls. While a few survey papers focusing on the applications of machine learning in networking have previously been published, a survey of similar scope and breadth is missing in literature. Through this paper, we advance the state of knowledge by carefully synthesizing the insights from these survey papers while also providing contemporary coverage of recent advances.

Our methodology demonstrates a proof of concept of the applicability of transfer learning for heliophysics, a machine learning technique where knowledge learned from one task is reused to perform a similar unsupervised learning task with additional fine tuning. We applied an unsupervised clustering algorithm, initially trained on data from the Magnetospheric Multiscale (MMS) mission at Earth, to MErcury Surface, Space ENvironment, GEochemistry, and Ranging (MESSENGER) observationsat Mercury to identify three distinct plasma regions: magnetosphere, magnetosheath, and solar wind. While our method requires modifications to the model from post-cleaning rules due to instrument effects, it allows for rapid classification using just a few examples to generate post-cleaning rules. Since there is no ground truth or standardized validation set to compare with, we compare our model’s result with published magnetopause and bow shock lists and find that the clustering algorithm is agreement with 67% of bow shock crossings and 74% of magnetopause crossings. These findings highlight the potential use of clustering algorithms across multiple planetary environments.

Book collections in libraries are an important means of information, but without proper assignment of books into appropriate categories, searching for books on similar topics is very troublesome for both librarians and readers. This is a difficult problem due to the analysis of large sets of real text data, such as the content of books. For this purpose, we propose to create an appropriate model system, the use of which will allow for automatic assignment of books to appropriate categories by analyzing the text from the content of the books. Our research was tested on a database consisting of 552 documents. Each document contains the full content of the book. All books are from Project Gutenberg in the Art, Biology, Mathematics, Philosophy, or Technology category. Well-known techniques of natural language processing (NLP) were used for the proper preprocessing of the book content and for data analysis. Then, two different machine learning approaches were used: classification (as supervised learning) and clustering (as unsupervised learning) in order to properly assign books to selected categories. Measures of accuracy, precision and recall were used to evaluate the quality of classification. In our research, good classification results were obtained, even above 90% accuracy. Also, the use of clustering algorithms allowed for effective assignment of books to categories.KeywordsNatural language processingBook classificationClusteringText analysisMachine learning

Customer relationship management (CRM) is the tool to enhance customer relationship in any business. Due to the exponential growth of data volume, in any field, it is significant to develop new techniques to discover the customer knowledge, automation of the system and moreover customer satisfaction to win customer lifetime value. CRM with machine learning could bring a catalytic change in business. Several supervised and unsupervised machine learning techniques are utilized to improve the customer experience and profitability of business. This paper reviews the available literature on the CRM with machine learning techniques for customer identification, customer attraction, and customer retention and customer development. This study reveals that supervised learning techniques are 48.48% utilized, unsupervised learning techniques are utilized 15.15%, and 9.09% utilized other techniques in CRM. Paradigm is also shifted toward the deep learning from machine learning as 28.28% text has been reported to deep learning. Decision tree-based algorithm and support vector machine algorithms are most utilized algorithm of supervised learning. E-commerce and telecommunication sectors are the most important areas identified with the exponential growth of the users and hence need a suitable machine learning techniques for customer satisfaction and business profitability.

Soybean yield components and agronomic traits are connected through physiological pathways that impose tradeoffs through genetic and environmental constraints. Our primary aim is to assess the interdependence of soybean traits by using unsupervised machine learning techniques to divide phenotypic associations into environmental and genetic associations. This study was performed on large scale, jointly analyzing 14 quantitative traits in a large multi-parental population designed for genetic studies. We collected phenotypes from 2012 to 2015 from a soybean nested association panel with 40 families of approximately 140 individuals each. Pearson and Spearman correlations measured phenotypic associations. A multivariate mixed linear model provided genotypic and environmental correlations. To evaluate relationships among traits, the study used principal component and undirected graphical models from phenotypic, genotypic, and environmental correlation matrices. Results indicate that high phenotypic correlation occurs when traits display both genetic and environmental correlations. In genetic terms, length of reproductive period, node number, and canopy coverage play important roles in determining yield potential. Optimal grain yield production occurs when the growing environment favors faster canopy closure and extended reproductive length. Environmental associations found among yield components give insight into the nature of yield component compensation. The use of unsupervised learning methods provides a good framework for investigating interactions among various quantitative traits and defining target traits for breeding.

Uncovering hidden subtypes in dementia: An unsupervised machine learning approach to dementia diagnosis and personalization of care.

Face recognition has always been a potential research area because of its demand for reliable identification of a human being especially in government and commercial sectors, such as security systems, criminal identification, border control, etc. where a large number of people interact with each other and/or with the system. The last two decades have witnessed many supervised and unsupervised learning techniques proposed by different researchers for the face recognition system. Principal component analysis (PCA), self‐organizing map (SOM), and independent component analysis (ICA) are the most widely used unsupervised learning techniques reported by research community. This article presents an analysis and comparison of these techniques. The article also includes two SOM processing methods global SOM (GSOM) and local SOM (LSOM) for performance evaluation along with PCA and ICA. We have used two different databases for our analysis. The simulation result establishes the supremacy of GSOM in general among all the unsupervised techniques. © 2010 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 20, 261–267, 2010

Kenya is experiencing a significant increase in the prevalence of non-communicable diseases (NCDs) such as cardiovascular diseases, hypertension, Type 2 diabetes, and certain cancers (bowel, lung, prostate, and uterine). This case is not unique to Kenya but is common in many Low and Middle-Income Countries (LMICs) in Africa. Many NCDs, are linked to diets high in added sugars, sodium, saturated fat, and low in fiber. There is a notable lack of information regarding the demographic differences among supermarket customers and their purchasing habits of healthy versus unhealthy foods in some parts of Africa. This gap in knowledge hinders the ability to connect grocery purchase patterns to NCDs, including obesity. Supermarkets in LMICs offer valuable demographic insights through grocery data. This research utilizes NOVA classification tool, data mining and unsupervised machine learning techniques to analyze grocery purchase patterns in 10 supermarkets across 5 counties in Kenya between 2022 and 2023. The apriori algorithm was used to create association rules and an analysis was done on the association rules to find out the relationship between demography (location, gender, and age) with purchase patterns. Individual data was collected along with transaction data, since the supermarkets logged transactions done by loyalty card customers. The main aim is to provide guidance to policymakers in public health. We collected 3 934 122 unique transactions and each transaction was associated with a customer who was identified with a unique customer ID. Findings from this research demonstrate that 53% of food purchases from these transactions were mainly industrially processed food items and males above the age of 50 years were the main consumers of these food items. The findings lead to the conclusion that this purchase trend has a chance of rising NCDs in older people. Therefore we recommend that policymakers adopt our recommendations to safeguard public health.

Hydrothermal alteration plays a fundamental role in controlling fluid–rock interaction, mineralization potential, and mass transfer in volcanic and subvolcanic systems. Conventional alteration studies rely on predefined geochemical indices and discrete alteration zones, which may oversimplify the inherently continuous nature of hydrothermal processes. In this study, we apply unsupervised machine learning techniques to whole-rock major and trace element geochemical data to objectively resolve alteration patterns and quantify relative alteration intensity without a priori classification schemes.A dataset comprising 15 rock samples and 63 major and trace element variables from two quarry sites within the Miocene Galatean Volcanic Province (GVP), Turkey, was analysed using principal component analysis (PCA), clustering validation techniques (silhouette and elbow methods), and correlation analysis with established hydrothermal alteration indices. PCA reveals that the first principal component (PC1), explaining ~42% of the total variance, systematically captures the geochemical signature of hydrothermal alteration. PC1 shows moderate-to-strong positive correlations with Ishikawa Alteration Index (AI; r = 0.59) and CIA′ (r = 0.72), and a strong negative correlation with a silicification ratio (r = −0.88), indicating progressive enrichment in Si and high-field-strength elements coupled with depletion of Fe–Mg–Al–Ca. Clustering analyses indicate that although two statistical end members can be identified, samples from both quarries define a continuous hydrothermal alteration trajectory rather than discrete geochemical groups, highlighting that alteration intensity—reflecting the degree of fluid–rock interaction and mass transfer—dominates the geochemical variability rather than quarry sites. Ordering samples along PC1 defines a data-driven, machine-learning-derived alteration index that quantitatively tracks progressive silicification from weakly altered to strongly altered compositions.These results demonstrate that unsupervised machine learning provides a robust and objective framework for deriving a data-driven alteration index and quantifying hydrothermal alteration intensity directly from multivariate geochemical data. PC1 defines a continuous alteration axis that captures progressive silicification coupled with Fe–Mg–Al–Ca depletion and is independently validated by its strong correlations with established alteration indices. By treating alteration as a continuous process rather than a set of discrete classes, this data-driven approach reveals gradual geochemical trends that are not readily captured by traditional classification schemes. The proposed methodology offers a transferable and reproducible framework for alteration mapping, comparative analysis of hydrothermal systems, and geochemical assessments in mineral exploration contexts.Keywords: Continuous hydrothermal alteration, Fluid–rock interaction, Galatean Volcanic Province, PC1-data-driven alteration index, Unsupervised geochemical pattern recognition

In the current context of rapid development of digital economy, enterprise financial management is facing increasingly complex internal and external environments, and the traditional financial management model has been difficult to meet the needs of intelligence, refinement and efficiency. This paper analyzes the status quo and development trend of enterprise financial informatization construction, and clarifies the important role of informatization in improving the efficiency of financial management; from the three dimensions of data-driven decision-making support, financial process optimization based on artificial intelligence, and the deep integration of informatization and intelligence, it discusses the key elements of the optimization of the intelligent financial management system and the path of achieving it; finally, it evaluates the effect of optimized intelligent financial management system through a typical case, and verifies that it can meet the demand of intelligent financial management and improve its efficiency. Finally, the optimized intelligent financial management system is evaluated through typical cases to verify its significant role in improving management efficiency, reducing operating costs and enhancing decision-making science. The study shows that promoting the organic combination of informationization construction and intelligent management can not only improve the overall level of enterprise financial management, but also provide an important guarantee for enterprises to achieve sustainable development.

Tropical cyclone (TC) track forecasts have improved in recent decades while intensity forecasts, particularly predictions of rapid intensification (RI), continue to show low skill. Many statistical methods have shown promise in predicting RI using environmental fields, although these methods rely heavily upon supervised learning techniques such as classification. Advances in unsupervised learning techniques, particularly those that integrate nonlinearity into the class separation problem, can improve discrimination ability for difficult tasks such as RI prediction. This study quantifies separability between RI and non-RI environments for 2004–16 Atlantic Ocean TCs using an unsupervised learning method that blends principal component analysis with k-means cluster analysis. Input fields consisted of TC-centered 1° Global Forecast System analysis (GFSA) grids (170 different variables and isobaric levels) for 3605 TC samples and five domain sizes. Results are directly compared with separability offered by operational RI forecast predictors for eight RI definitions. The unsupervised learning procedure produced improved separability over operational predictors for all eight RI definitions, five of which showed statistically significant improvement. Composites from these best-separating GFSA fields highlighted the importance of mid- and upper-level relative humidity in identifying the onset of short-term RI, whereas long-term, higher-magnitude RI was generally associated with weaker absolute vorticity. Other useful predictors included optimal thermodynamic RI ingredients along the mean trajectory of the TC. The results suggest that the orientation of a more favorable thermodynamic environment relative to the TC and midlevel vorticity magnitudes could be useful predictors for RI.

Fuzzy clustering is a popular unsupervised learning method that is used in cluster analysis. Fuzzy clustering allows a data point to belong to two or more clusters. Fuzzy c-means is the most well-known method that is applied to cluster analysis, however, the shortcoming is that the number of clusters need to be predefined. This paper proposes a clustering approach based on Particle Swarm Optimization (PSO). This PSO approach determines the optimal number of clusters automatically with the help of a threshold vector. The algorithm first randomly partitions the data set within a preset number of clusters, and then uses a reconstruction criterion to evaluate the performance of the clustering results. The experiments conducted demonstrate that the proposed algorithm automatically finds the optimal number of clusters. Furthermore, to visualize the results principal component analysis projection, conventional Sammon mapping, and fuzzy Sammon mapping were used

Developing the utilized intelligent systems is increasingly important to learn effective text representations, especially extract the sentence features. Numerous previous studies have been concentrated on the task of sentence representation learning based on deep learning approaches. However, the present approaches are mostly proposed with the single task or replied on the labeled corpus when learning the embedding of the sentences. In this paper, we assess the factors in learning sentence representation and propose an efficient unsupervised learning framework with multi-task learning (USR-MTL), in which various text learning tasks are merged into the unitized framework. With the syntactic and semantic features of sentences, three different factors to some extent are reflected in the task of the sentence representation learning that is the wording, or the ordering of the neighbored sentences of a target sentence in other words. Hence, we integrate the word-order learning task, word prediction task, and the sentence-order learning task into the proposed framework to attain meaningful sentence embeddings. Here, the process of sentence embedding learning is reformulated as a multi-task learning framework of the sentence-level task and the two word-level tasks. Moreover, the proposed framework is motivated by an unsupervised learning algorithm utilizing the unlabeled corpus. Based on the experimental results, our approach achieves the state-of-the-art performances on the downstream natural language processing tasks compared to the popular unsupervised representation learning techniques. The experiments on representation visualization and task analysis demonstrate the effectiveness of the tasks in the proposed framework in creating reasonable sentence representations proving the capacity of the proposed unsupervised multi-task framework for the sentence representation learning.

Background: Atrial fibrillation Better Care (ABC) pathway is recommended by guidelines on atrial fibrillation (AF) and exerts a protective role against adverse outcomes of AF patients. We hypothesize that cluster analysis, an unsupervised machine learning technique, could comprehensively evaluate multiple clinical characteristics of patients, and define clusters of ABC criteria efficacy in patients with AF. Methods: We used data from an observational cohort that included 2,016 patients with AF. We utilized 46 baseline variables for cluster analysis and got the optimal clusters through the K-prototypes algorithm. We evaluated the management patterns and adverse outcomes of identified phenotypes. We assessed the effectiveness of the ABC criteria in reducing adverse outcomes of these phenotypes. Results: Cluster analysis identified AF patients into three distinct groups with markedly different clinical characteristics and outcomes. The clusters were as followed: Cluster 1, old patients with atherosclerotic-comorbidities (n = 964); Cluster 2, young females with valve-comorbidities (n = 407), and Cluster 3, paroxysmal AF patients with low comorbidities (n = 644). The clusters showed significant differences in MACNE, all-cause death, stroke, cardiovascular death, and hospital readmission for heart failure. All clusters showed that full adherence to the ABC pathway was associated with a significant reduction in the risk of MACNE (all P< 0.05). Adherence to the different ‘A’/’B’/’C’ criteria alone showed differential clinic impact for three clusters. Conclusion: Cluster analysis of the Chinese AF cohort further elucidated the heterogeneity of AF. We proposed suggestions for optimizing risk stratification and integrated management of AF patients.