With the rapid growth of online property rental and sale platforms, the prevalence of fake real estate listings has become a significant concern. These deceptive listings waste time and effort for buyers and sellers and pose potential risks. Therefore, developing effective methods to distinguish genuine from fake listings is crucial. Accurately identifying fake real estate listings is a critical challenge, and clustering analysis can significantly improve this process. While clustering has been widely used to detect fraud in various fields, its application in the real estate domain has been somewhat limited, primarily focused on auctions and property appraisals. This study aims to fill this gap by using clustering to classify properties into fake and genuine listings based on datasets curated by industry experts. This study developed a K-means model to group properties into clusters, clearly distinguishing between fake and genuine listings. To assure the quality of the training data, data pre-processing procedures were performed on the raw dataset. Several techniques were used to determine the optimal value for each parameter of the K-means model. The clusters are determined using the Silhouette coefficient, the Calinski-Harabasz index, and the Davies-Bouldin index. It was found that the value of cluster 2 is the best and the Camberra technique is the best method when compared to overlapping similarity and Jaccard for distance. The clustering results are assessed using two machine learning algorithms: Random Forest and Decision Tree. The observational results have shown that the optimized K-means significantly improves the accuracy of the Random Forest classification model, boosting it by an impressive 96%. Furthermore, this research demonstrates that clustering helps create a balanced dataset containing fake and genuine clusters. This balanced dataset holds promise for future investigations, particularly for deep learning models that require balanced data to perform optimally. This study presents a practical and effective way to identify fake real estate listings by harnessing the power of clustering analysis, ultimately contributing to a more trustworthy and secure real estate market.

BACKGROUND: Prior typing methods fail to provide predictive insights into surgical complexities for extrahepatic choledochal cyst (ECC). This study aims to establish a new classification system for ECC through clustering of imaging results. Additionally, it seeks to compare the differences among the identified ECC types and assess the levels of surgical difficulty.
METHODS: The imaging data of 124 patients were automatically grouped through a K-means clustering analysis. According to the characteristics of the new grouping, corrections and interventions were carried out to establish a new classification. Demographic data, clinical presentations, surgical parameters, complications, reoperation, and prognostic indicators were analyzed according to different types. Factors contributing to prolonged surgical time were also evaluated.
RESULTS: A new classification system of ECC: Type A (upper segment), Type B (middle segment), Type C (lower segment), and Type D (entire bile duct). The incidences of comorbidities (calculus or infection) were significantly different (P = 0.000, P = 0.002). Additionally, variations in the incidence of postoperative biliary stricture were statistically significant (P = 0.046). The operative time was significantly different between groups (P = 0.001). Age, BMI > 30, classification, and the presence of combined stones exhibit a significant association with prolonged operative time (P = 0.002, P = 0.000, P = 0.011, P = 0.011).
CONCLUSIONS: In conclusion, our utilization of machine learning-driven cluster analysis has enabled the creation of a novel extrahepatic biliary dilatation typology. This classification, in conjunction with factors like age, combined stone occurrence, and obesity, significantly influences the complexity of laparoscopic choledochal cyst surgery, offering valuable insights for improved surgical treatment.

UNASSIGNED: Understanding the impact of different lifestyle trajectories on health preservation and disease risk is crucial for effective interventions.
UNASSIGNED: This study analyzed lifestyle engagement over five years in 3,013 healthy adults aged 40-70 from the Barcelona Brain Health Initiative using K-means clustering. Nine modifiable risk factors were considered, including cognitive, physical, and social activity, vital plan, diet, obesity, smoking, alcohol consumption, and sleep. Self-reported diagnoses of new diseases at different time-points after baseline allowed to explore the association between these five profiles and health outcomes.
UNASSIGNED: The data-driven analysis classified subjects into five lifestyle profiles, revealing associations with health behaviors and risk factors. Those exhibiting high scores in health-promoting behaviors and low-risk behaviors, demonstrate a reduced likelihood of developing diseases (p < 0.001). In contrast, profiles with risky habits showed distinct risks for psychiatric, neurological, and cardiovascular diseases. Participant\'s lifestyle trajectories remained relatively stable over time.
UNASSIGNED: Our findings have identified risk for distinct diseases associated to specific lifestyle patterns. These results could help in the personalization of interventions based on data-driven observation of behavioral patterns and policies that promote a healthy lifestyle and can lead to better health outcomes for people in an aging society.

This study investigates the impact of Free-to-Publish (F2P) versus Pay-to-Publish (P2P) models in dermatology journals, focusing on their differences in terms of journal metrics, Article Processing Charges (APCs), and Open Access (OA) status. Utilizing k-means clustering, the research evaluates dermatology journals based on SCImago Journal Rankings (SJR), H-Index, and Impact Factor (IF), and examines the correlation between these metrics, APCs, and OA status (Full or Hybrid). Data from the SCImago Journal Rank and Journal Citation Report databases were used, and metrics from 106 journals were normalized and grouped into three tiers.The study reveals a higher proportion of F2P journals, especially in higher-tier journals, indicating a preference for quality-driven research acceptance. Conversely, a rising proportion of P2P journals in lower tiers suggests potential bias towards the ability to pay. This disparity poses challenges for researchers from less-funded institutions or those early in their careers. The study also finds significant differences in APCs between F2P and P2P journals, with hybrid OA being more common in F2P.Conclusively, the study highlights the disparities in dermatology journals between F2P and P2P models and underscores the need for further research into authorship demographics and institutional affiliations in these journals. It also establishes the effectiveness of k-means clustering as a standardized method for assessing journal quality, which can reduce reliance on potentially biased individual metrics.

The work presented in this paper is focused on the largest marine disaster to have occurred in the Indian Ocean due to the breakup of the container tanker ship X-Press Pearl. In order to identify the oil spill and its temporal evolution, a recently proposed damping ratio (DR) index is employed. To derive the DR, a data-driven GMM-EM clustering method optimized by stochastic ordering of the resulting classes in Sentinel 1 SAR time series imagery is proposed. A ship-born oil spill site is essentially considered to consist of three subsites: oil, open sea, and ship. The initial site probability densities were determined by using k-means clustering. In addition to the clustering method, two histogram-based approaches, namely contextual peak thresholding (CPT) and contextual peak ordering (CPO), were also formulated and presented. The improved histogram peak detection methods take into account spatial and contextual dependencies. The similarity of the marginal probability densities of the open sea and the oil classes makes it difficult to quantify the DR values to show the level of dampening. In the study, we show that reasonable class separability to correctly determine the σVV0,seaθ is possible by using GMM clustering. Resulting class separability\'s are also reported using JM and ML distances. The methods tested show the range of derived DR values stays significantly within similar ranges to each other. The outcomes were tested with the ground-based surveys conducted during the disaster for oil spill sites and other chemical compounds. The proposed methods are simple to execute, robust, and fully automated. Further, they do not require masking the oil or the selection of high-confidence water pixels manually.

BACKGROUND: Hypospadias phenotype assessment determines if the anatomy is favorable for reconstruction. Glans-Urethral Meatus-Shaft (GMS) has been adopted in an effort to standardize hypospadias classification. While extremely subjective, GMS has been widely used to classify the severity of the phenotype to predict surgical outcomes. The use of digital image analysis has proven to be feasible and prior efforts by our team have demonstrated that machine learning algorithms can emulate an expert\'s assessment of the phenotype. Nonetheless, the creation of these image recognition algorithms is highly subjective. In order to reduce a subjective input in the evaluation of the phenotype, we propose a novel approach to analyze the anatomy using digital image pixel analysis and to compare the results using the GMS score. Our hypothesis is that pixel cluster segmentation can discriminate between favorable and unfavorable anatomy.
OBJECTIVE: To evaluate whether image segmentation and digital pixel analysis are able to analyze favorable vs unfavorable hypospadias anatomy in a less subjective manner than GMS score.
METHODS: A total of 148 patients with different types of hypospadias were classified by 1 of 5 independent experts following the GMS score into \"favorable\" (GG), \"moderately favorable\" (GM) and \"unfavorable\" (GP) glans. From there, 592 images were generated using digital image segmentation. 584 were included for final analysis due to certain images being excluded for poor image quality or inadequate capture of target anatomy. For each image, the region of interest was segmented separately by two evaluators into \"glans,\" \"urethral plate,\" \"foreskin\" and \"periurethral plate\". The values obtained for each segmented region using machine-learning statistical pixel k-means cluster analysis were analyzed and compared to the GMS score given to that image using an ANOVA analysis.
RESULTS: Analysis of image segmentation demonstrated that k-means pixel cluster analysis discriminated \"favorable\" vs \"unfavorable\" urethral plates. There was a significant difference between scores when comparing the GG and GM groups (p = 0.03) and GG and GP groups (p = 0.05). Pixel cluster analysis could not discriminate between \"moderately favorable\" and \"unfavorable\" urethral plates.
CONCLUSIONS: Through our analysis, we found significant pairwise difference for different tissue qualities. Digital image segmentation and statistical k-means cluster analysis can discriminate anatomical features in a similar way to the GMS score. Future research can target discerning between different tissue qualities in an effort to predict surgical outcomes for hypospadias repair.

Compared to supervised machine learning (ML), the development of feature selection for unsupervised ML is far behind. To address this issue, the current research proposes a stepwise feature selection approach for clustering methods with a specification to the Gaussian mixture model (GMM) and the k-means. Rather than the existing GMM and k-means which are carried out based on all the features, the proposed method selects a subset of features to implement the two methods, respectively. The research finds that a better result can be obtained if the existing GMM and k-means methods are modified by nice initializations. Experiments based on Monte Carlo simulations show that the proposed method is more computationally efficient and the result is more accurate than the existing GMM and k-means methods based on all the features. The experiment based on a real-world dataset confirms this finding.

The water reuse facilities of industrial parks face the challenge of managing a growing variety of wastewater sources as their inlet water. Typically, this clustering outcome is designed by engineers with extensive expertise. This paper presents an innovative application of unsupervised learning methods to classify inlet water in Chinese water reuse stations, aiming to reduce reliance on engineer experience. The concept of \'water quality distance\' was incorporated into three unsupervised learning clustering algorithms (K-means, DBSCAN, and AGNES), which were validated through six case studies. Of the six cases, three were employed to illustrate the feasibility of the unsupervised learning clustering algorithm. The results indicated that the clustering algorithm exhibited greater stability and excellence compared to both artificial clustering and ChatGPT-based clustering. The remaining three cases were utilized to showcase the reliability of the three clustering algorithms. The findings revealed that the AGNES algorithm demonstrated superior potential application ability. The average purity in six cases of K-means, DBSCAN, and AGNES were 0.947, 0.852, and 0.955, respectively.

Informed by the biopsychosocial framework, our study uses the Chinese Longitudinal Healthy Longevity Survey (CLHLS) dataset to examine cognitive function trajectories among the oldest-old (80+). Employing K-means clustering, we identified two latent groups: High Stability (HS) and Low Stability (LS). The HS group maintained satisfactory cognitive function, while the LS group exhibited consistently low function. Lasso regression revealed predictive factors, including socioeconomic status, biological conditions, mental health, lifestyle, psychological, and behavioral factors. This data-driven approach sheds light on cognitive aging patterns and informs policies for healthy aging. Our study pioneers non-parametric machine learning methods in this context.

With the transformation and upgrading of industries, the environmental problems caused by industrial residual contaminated sites are becoming increasingly prominent. Based on actual investigation cases, this study analyzed the soil pollution status of a remaining sites of the copper and zinc rolling industry, and found that the pollutants exceeding the screening values included Cu, Ni, Zn, Pb, total petroleum hydrocarbons and 6 polycyclic aromatic hydrocarbon monomers. Based on traditional analysis methods such as the correlation coefficient and spatial distribution, combined with machine learning methods such as SOM + K-means, it is inferred that the heavy metal Zn/Pb may be mainly related to the production history of zinc rolling. Cu/Ni may be mainly originated from the production history of copper rolling. PAHs are mainly due to the incomplete combustion of fossil fuels in the melting equipment. TPH pollution is speculated to be related to oil leakage during the industrial use period and later period of vehicle parking. The results showed that traditional analysis methods can quickly identify the correlation between site pollutants, while SOM + K-means machine learning methods can further effectively extract complex hidden relationships in data and achieve in-depth mining of site monitoring data.