Clustering Techniques Research Articles

Data Management Analysis for Predicting Stroke using RapidMiner

Stroke are known as the second most leading cause of death. Because of this, data mining techniques are already being used to predict patients that may have stroke. Therefore, we are doing a study to try using data mining techniques using RapidMiner to find information or patterns regarding stroke from a dataset obtained from Kaggle. Three data mining techniques are used in this study, that is classification using decision trees, association rule using FP-Growth algorithm, and clustering technique using k-Means algorithm. Using RapidMiner, we are able to process the dataset using the operators provided in the application. As the result, we found out that due to an unbalanced data, the decision tree model made were only able to predict 68,75% patients as having stroke. With the association rule technique, we found out that most attributes in the dataset does not really associated with each other. With the clustering technique, we were able to group up patients and found out that most patients that have stroke are averaged in the age of 58, with 31 bmi, and 201 average glucose level.

A Comparative Study of Multivariate Clustering Algorithms for Climate Variables and Chickpea Production in Dharwad District of Karnataka, India

India's agriculture sector holds immense significance in its economy, and this study delves into how different clustering techniques can enhance our comprehension of the interplay between weather patterns and chickpea production in Dharwad district, specifically Density-Based Spatial Clustering of Applications with Noise (DBSCAN), K-Means, and Hierarchical clustering methods to discern underlying patterns within climate variables and chickpea yield data. Leveraging historical weather data spanning an extensive 42-year period and crop yield records were employed to train and validated. The findings illuminate that K-Means clustering consistently outperforms DBSCAN and Hierarchical clustering when evaluated through a variety of validity indices, including the Silhouette Coefficient, Calinski-Harabasz Index, Davies-Bouldin Index, and the determination of the optimal number of clusters. In essence, India's pivotal agricultural sector dynamics and the intricate relationship between climatic factors and chickpea production in Dharwad district are elucidated effectively by the superiority of K-Means clustering in this study.

Sense through time: diachronic word sense annotations for word sense induction and Lexical Semantic Change Detection

AbstractThere has been extensive work on human word sense annotation, i.e., manually labeling word uses in natural texts according to their senses. Such labels were primarily created for the tasks of Word Sense Disambiguation (WSD) and Word Sense Induction (WSI). However, almost all datasets annotated with word senses are synchronic datasets, i.e., contain texts created in a relatively short period of time and often do not provide the creation date of the texts. This ignores possible applications in diachronic-historic settings, where the aim is to induce or disambiguate historical word senses or changes in senses across time. To facilitate investigations into historical WSD and WSI and to establish connections with the task of Lexical Semantic Change Detection (LSCD), there is a crucial need for historical word sense-annotated data. Hence, we created a new reliable diachronic WSD/WSI dataset ‘DWUG DE Sense’. We describe the preparation and annotation and analyze central statistics. We then describe a thorough evaluation of different prediction systems for jointly solving both WSI and LSCD tasks. All our systems are based on a state-of-the-art architecture that combines Word-in-Context models and graph clustering techniques with different hyperparameter settings. Our findings reveal that using the WSI task as optimization criterion yields better results for both tasks even when the LSCD task is the focal point of optimization. This underscores that although both tasks are related, WSI seems to be more general and able to incorporate the LSCD task.

Structural mechanism of glass transition uncovered by unsupervised machine learning

Uncovering the structural origins of the ubiquitous dynamic arrest phenomenon at the glass transition has long been a challenge due to the difficulty in identifying a rational structural representation from a disordered medium. To address this challenge, we propose a novel approach based on unsupervised learning to define a set of structural fingerprints. In this approach, complex local atomic environments, ranging from short to medium range, are captured by the discretized radial distribution function and projected onto a simple two-dimensional space using a neural network-based autoencoder. This two-dimensional space is characterized by two static structural indicators, P1 and P2, providing a comprehensive and user-friendly representation of the mysterious “glassy structure”. By employing Gaussian mixture modeling, the structural space is autonomously divided into three sections, each representing a unique cluster with similar environments. These indicators not only elucidate the glass transition but also allow for the quantitative prediction of activation barriers for local structural excitations. Furthermore, the unsupervised clustering technique can distinguish between the structural features of “hard zones” and “soft zones”, as well as recently proposed superfast “liquid-like” atoms in glass. This unsupervised machine learning approach demonstrates the utility of seemingly agnostic local structure in amorphous materials, offering insights into the long-sought structural origins of the glass transition.

Low-Rank Robust Subspace Tensor Clustering for Metro Passenger Flow Modeling

Tensor clustering has become an important topic, specifically in spatiotemporal modeling, because of its ability to cluster spatial modes (e.g., stations or road segments) and temporal modes (e.g., time of day or day of the week). Our motivating example is from subway passenger flow modeling, where similarities between stations are commonly found. However, the challenges lie in the innate high-dimensionality of tensors and also the potential existence of anomalies. This is because the three tasks, that is, dimension reduction, clustering, and anomaly decomposition, are intercorrelated with each other, and treating them in a separate manner will render a suboptimal performance. Thus, in this work, we design a tensor-based subspace clustering and anomaly decomposition technique for simultaneous outlier-robust dimension reduction and clustering for high-dimensional tensors. To achieve this, a novel low-rank robust subspace clustering decomposition model is proposed by combining Tucker decomposition, sparse anomaly decomposition, and subspace clustering. An effective algorithm based on Block Coordinate Descent is proposed to update the parameters. Prudent experiments prove the effectiveness of the proposed framework via the simulation study, with a gain of +25% clustering accuracy over benchmark methods in a hard case. The interrelations of the three tasks are also analyzed via ablation studies, validating the interrelation assumption. Moreover, a case study in station clustering based on real passenger flow data is conducted, with quite valuable insights discovered. History: Bianca Maria Colosimo served as the senior editor for this article. Funding: Dr. Yan is partially funded by DOE [DE-EE0009354] and NSF [CMMI 2316654]. Dr. Fugee is partially funded with the RGC [GRF 16201718 and 16216119]. The authors appreciate the help from the Hong Kong MTR Co. research, marketing, and customer service teams. Data Ethics & Reproducibility Note: The code capsule is available on Code Ocean at https://codeocean.com/capsule/6536164/tree and in the e-Companion to this article (available at https://doi.org/10.1287/ijds.2022.0028 ).

Predictive Maintenance in IoT-Monitored Systems for Fault Prevention

This paper focuses on predictive maintenance for simple machinery systems monitored by the Internet of Things (IoT). As these systems can be challenging to model due to their complexity, diverse typologies, and limited operational lifespans, traditional predictive maintenance approaches face obstacles due to the lack of extensive historical data. To address this issue, we propose a novel clustering-based process that identifies potential machinery faults. The proposed approach lies in empowering decision-makers to define predictive maintenance policies based on the reliability of the proposed fault classification. Through a case study involving real sensor data from the doors of a transportation vehicle, specifically a bus, we demonstrate the practical applicability and effectiveness of our method in preemptively preventing faults and enhancing maintenance practices. By leveraging IoT sensor data and employing clustering techniques, our approach offers a promising avenue for cost-effective predictive maintenance strategies in simple machinery systems. As part of the quality assurance, a comparison between the predictive maintenance model for a simple machinery system, pattern recognition neural network, and support vector machine approaches has been conducted. For the last two methods, the performance is lower than the first one proposed.

Clustering and Interpretation of time-series trajectories of chronic pain using evidential c-means

The most well-known unsupervised classification algorithms allow for the identification of hard or probabilistic partitions. However, when working with complex datasets such as those found in the healthcare domain, it is important to note that these partitions may have limitations, particularly when considering the uncertainty associated with medical data. Indeed, these algorithms do not account for modeling outliers, imprecise, or uncertain observations. In this study, we focused on analyzing time series of barometers attributes related to pain intensity in individuals with chronic pain. Our goal was to identify different typologies of care trajectories, highlighting distinct trajectories of chronic pain through clustering techniques. We propose a soft clustering approach based on feature extraction and selection from sequential data, aiming to improve interpretability and enhance the performance of the clustering procedure. Time series feature extraction and selection effectively handles complex and noisy data, improving interpretability and clustering performance. Evidential soft clustering models data uncertainty and imprecision, providing a better representation of nuances and variations in complex, raw data. The first step involves extracting features from the available time series and selecting the most important attributes. For this purpose, we use a time series feature extractor (Tsfresh) and unsupervised feature selection methods, including unsupervised Random Forest, Laplacian Score, and unsupervised Spectral Feature Selection. The second step involves using the evidential c-means (ECM) clustering algorithm on the extracted attributes. The ECM method, based on belief functions, allows for generating a credal partition that has the ability to model various forms of uncertainty. This partition can then be transformed into a hard partition to study individuals based on this uncertainty criterion. The results reveal the existence of two clusters of chronic pain related to discomfort and well-being, exhibiting excellent separability and compactness. Additionally, an uncertain cluster emerges grouping patients with intermediate characteristics. The interpretability of the determined partitions through descriptive analysis, statistical tests, and repeated measures multinomial regression on clinical and demographic data allowed us to determine the profile of patients in the identified trajectories.

An efficient optimization approach with mobility management for enhanced QoS and secure communication in flying adhoc networks

In biomedical Applications, Network plays a vital role to transmit the health-related information from patient to doctors which helps to save the human lives. During the data transmission, there is the possibility of data loss, delayed delivery, malicious attack and that leads to huge loss in human lives. To overcome this issue, a new technique Smart & secure Flying Adhoc Networks (FANET) architecture was proposed for the Quality-of-Service (QoS) improvement in biomedical applications. This research work helps society to save human lives during any emergency. With the integration of optimization and clustering techniques, the design configuration of the proposed architecture includes mobility management and secure transmission. The first phase includes sensing the patient's information by using Arduino IDE hardware. The sensed data will be transmitted through different types of networks including mobile ad-hoc networks, vehicular ad-hoc networks and flying ad-hoc networks. The routing protocol concentrates on clustering formation, mobility management, secure transmission and optimization. When compared to other type of networks, the proposed Smart & secure Flying Adhoc Networks (FANET) improves the network performance. The quality-of-service was achieved using the parameters such as network throughput, latency, delivery rate, routing overhead and control overhead.

Social-Cultural Factors Influence on Management of Shared Sanitation, in Nakuru Town West Slums

Sustainable Development Goal (SDG) target 6.2 calls for 'adequate and equitable sanitation for all.' However, rapid urbanization in developing countries has led to the growth of slums, where access to private toilets is impractical. Evident studies showed that shared toilets are more often poorly managed with adverse harmful health risks such as diarrheal diseases, environmental pollution, and economic deprivations. The study's specific objective was to establish social-cultural factors influencing the management of shared sanitation in Nakuru Town's west slums, Nakuru County, Kenya. The study adopted a convergent parallel mixed-method design involving 288 household heads selected through a cluster and random sampling technique. Data was collected using a structured questionnaire and interview guides. Descriptive statistics and inferential statistics was computed. The study findings indicated that the majority, 74.7%, of shared sanitation was poorly managed. The study established statistically significant associations of secondary education (p=0.024), marital status (p=0.025), user satisfaction (p=0.001), household sharing (p=0.000), social norms (p=0.001), cultural beliefs (p=0.002), Rituals on use and cleaning toilet (p=0.038), Privacy and modesty concerns (p=0.002) and management of shared sanitation. In conclusion, a large proportion of households in slums used poorly managed shared sanitation due to the influence of multiple factors like users' satisfaction, Household sharing, marital status, social norms, and cultural beliefs, thus polluting the public health of the place. The study recommended that the Government and all other agencies implementing sanitation promotion interventions should understand the social-cultural practices of the target communities and articulate this knowledge to tailor sanitation initiatives effectively. Future studies should focus on implementing and assessing the effectiveness of behavioral interventions.

Mesoscopic structure of the stock market and portfolio optimization

AbstractThe idiosyncratic and systemic components of market structure have been shown to be responsible for the departure of the optimal mean-variance allocation from the heuristic ‘equally weighted’ portfolio. In this paper, we exploit clustering techniques derived from Random Matrix Theory to study a third, intermediate (mesoscopic) market structure that turns out to be the most stable over time and provides important practical insights from a portfolio management perspective. First, we illustrate the benefits, in terms of predicted and realized risk profiles, of constructing portfolios by filtering out both random and systemic co-movements from the correlation matrix. Second, we redefine the portfolio optimization problem in terms of stock clusters that emerge after filtering. Finally, we propose a new wealth allocation scheme that attaches equal importance to stocks belonging to the same community and show that it further increases the reliability of the constructed portfolios. Results are robust across different time spans, cross sectional dimensions and set of constraints defining the optimization problem.

Development and validation of the Upstream Social Interaction Risk Scale (U-SIRS-13): a scale to assess threats to social connectedness among older adults.

Social interactions are essential to social connectedness among older adults. While many scales have been developed to measure various aspects of social connectedness, most are narrow in scope, which may not be optimally encompassing, practical, or relevant for use with older adults across clinical and community settings. Efforts are needed to create more sensitive scales that can identify "upstream risk," which may facilitate timey referral and/or intervention. The purposes of this study were to: (1) develop and validate a brief scale to measure threats to social connectedness among older adults in the context of their social interactions; and (2) offer practical scoring and implementation recommendations for utilization in research and practice contexts. A sequential process was used to develop the initial instrument used in this study, which was then methodologically reduced to create a brief 13-item scale. Relevant, existing scales and measures were identified and compiled, which were then critically assessed by a combination of research and practice experts to optimize the pool of relevant items that assess threats to social connectedness while reducing potential redundancies. Then, a national sample of 4,082 older adults ages 60 years and older completed a web-based questionnaire containing the initial 36 items about social connection. Several data analysis methods were applied to assess the underlying dimensionality of the data and construct measures of different factors related to risk, including item response theory (IRT) modeling, clustering techniques, and structural equation modeling (SEM). IRT modeling reduced the initial 36 items to create the 13-item Upstream Social Interaction Risk Scale (U-SIRS-13) with strong model fit. The dimensionality assessment using different clustering algorithms supported a 2-factor solution to classify risk. The SEM predicting highest risk items fit exceptionally well (RMSEA = 0.048; CFI = 0.954). For the 13-item scale, theta scores generated from IRT were strongly correlated with the summed count of items binarily identifying risk (r = 0.896, p < 0.001), thus supporting the use of practical scoring techniques for research and practice (Cronbach's alpha = 0.80). The U-SIRS-13 is a multidimensional scale with strong face, content, and construct validity. Findings support its practical utility to identify threats to social connectedness among older adults posed by limited physical opportunities for social interactions and lacking emotional fulfillment from social interactions.

An efficient network clustering approach using graph-boosting and nonnegative matrix factorization

Network clustering is a critical task in data analysis, aimed at uncovering the underlying structure and patterns within complex networks. Traditional clustering methods often struggle with large-scale and noisy data, leading to suboptimal results. Also, the efficiency of positive samples in network clustering depends on the carefully constructed data augmentation, and the pre-training process of the model deals with large-scale data. To address these issues, in this paper, we introduce an efficient network clustering approach that leverages Graph-Boosting and Nonnegative Matrix Factorization to enhance clustering performance (GBNMF). Our algorithm addresses the limitations of traditional clustering techniques by incorporating the strengths of graph-boosting, which iteratively improves the quality of clusters, and Nonnegative Matrix Factorization (NMF), which effectively captures latent structures within the data. We validate our algorithm through extensive experiments on various benchmark network datasets, demonstrating significant improvements in clustering accuracy and robustness. The proposed algorithm not only achieves superior clustering results but also exhibits remarkable computational efficiency, making it a valuable tool for large-scale network analysis applications.

Methods for phenotyping adult patients with acute kidney injury: a systematic review.

Acute kidney injury (AKI) is a multifaceted disease characterized by diverse clinical presentations and mechanisms. Advances in artificial intelligence have propelled the identification of AKI subphenotypes, enhancing our capacity to customize treatments and predict disease trajectories. We conducted a systematic review of the literature from 2017 to 2022, focusing on studies that utilized machine learning techniques to identify AKI subphenotypes in adult patients. Data were extracted regarding patient demographics, clustering methodologies, discriminators, and validation efforts from selected studies. The review highlights significant variability in subphenotype identification across different populations. All studies utilized clinical data such as comorbidities and laboratory variables to group patients. Two studies incorporated biomarkers of endothelial activation and inflammation into the clinical data to identify subphenotypes. The primary discriminators were comorbidities and laboratory trajectories. The association of AKI subphenotypes with mortality, renal recovery and treatment response was heterogeneous across studies. The use of diverse clustering techniques contributed to variability, complicating the application of findings across different patient populations. Identifying AKI subphenotypes enables clinicians to better understand and manage individual patient trajectories. Future research should focus on validating these phenotypes in larger, more diverse cohorts to enhance their clinical applicability and support personalized medicine in AKI management.

Optimizing Health Pattern Recognition Particle Swarm Optimization Approach for Enhanced Neural Network Performance

Health pattern recognition is vital for advancing personalized healthcare interventions. This research introduces a synergistic approach, combining Fuzzy C-Means clustering with Particle Swarm Optimization (PSO), to optimize the hyperparameters of an Artificial Neural Network (ANN) and enhance health pattern recognition. Leveraging key features such as 'Smoker,' 'BMI,' and 'GenHlth,' Fuzzy C-Means reveals distinctive health clusters, providing nuanced insights into diverse health profiles within the dataset. Subsequently, the PSO algorithm systematically optimizes critical ANN hyperparameters, significantly decreasing the training loss to 0.004. This reduction underscores the effectiveness of the optimization process, indicating improved learning and predictive capabilities of the ANN. The proposed methodology not only refines health pattern recognition but also holds promise for personalized healthcare analytics. The identified clusters offer actionable insights for tailored interventions, addressing specific health profiles within the population. This research contributes to the evolving landscape of healthcare analytics by integrating advanced clustering and optimization techniques, paving the way for more effective and individualized healthcare strategies.

Cluster Analysis of the Recent Interdecadal Variation in the Tripole Landfall Pattern of Tropical Cyclones in East Asia

Abstract Along the East Asian coast, the interdecadal variation in tropical cyclone (TC) landfall activities in the early 2010s shows a tripole change pattern characterized by significantly increased (decreased) TC landfalls to the north of 30°N and to the south of 20°N (between 20° and 30°N). The increased TC landfalls in the East Asian subregion north to 30°N mean greater TC damage in the most populous regions, including southeastern China, the Korean Peninsula, and Japan, since the early 2010s. The present work uses an objective clustering technique to analyze the internal dynamical causes of this interdecadal variation. The increased TC landfall activities in the East Asian subregion north of 30°N are attributed to the increase in TCs with a northwestward-moving track (cluster defined as C1), whereas the increased TC landfall activities to the south of 20°N and the decrease between 20° and 30°N are mainly due to the southward shift of TCs with a westward-moving track (cluster defined as C2). This work focuses on different TC track groups (C1 and C2) based on daily data, showing that the interdecadal changes in the synoptic-scale monsoon trough and subtropical high provide important environment for different groups of TCs. The ascending phase of the PDO with significant warming over the central Pacific favors the interdecadal tripole changes in TC landfalls.

Integrating UAV imagery and machine learning via Geographic Object Based Image Analysis (GEOBIA) for enhanced monitoring of Yucca gloriosa in Mediterranean coastal dunes

Effective monitoring and early detection of invasive alien plant species (IAPs) are crucial for mitigating their spread and safeguarding native habitats. Unmanned Aerial Vehicles (UAVs) offer a cost-efficient solution, providing high resolution images. In this study, we aimed to develop a semi-automated methodology using a machine learning algorithm, spatial metrics, and clustering techniques on UAV images to monitor, map, and suggest management measures to counteract Yucca gloriosa, an invasive plant colonizing coastal fixed dunes in central Italy.UAV flights were conducted using two drones: one for the visible spectrum and the other for multispectral bands (Blue, Green, Red, Red Edge, and Near Infrared) along with a Digital Surface Model (DSM). Derived vegetation indices were also utilized. For mapping Y. gloriosa distribution, a Geographic Object Based Image Analysis (GEOBIA) approach was applied to the orthophoto segmentation, followed by a Random Forest algorithm in a training phase, considering three variable combinations (DSM + vegetation indices, DSM + spectral bands, DSM + mixed variables). The most accurate Y. gloriosa map was used to suggest management measures combining the spatial pattern of invaded patches (size, height, isolation level, and aggregation degree) and a mixed clustering approach (hierarchical and partitioning).The results highlighted that the most accurate prediction map was based on the DSM + mixed variables dataset, showing the important role of using a combination of spectral bands and vegetation indices. In all three cases, the DSM emerged as the pivotal variable for discriminating Y. gloriosa from the surrounding environment. Additionally, our results demonstrate the advantages of incorporating vegetation indices in discerning the target invasive alien plant (IAP) from the broader environment, particularly considering its distinctive photosynthesis process and biomass production. From a managerial standpoint, our pilot study indicates that the UAV-based mapping methodology represents an optimal balance between field efforts and costs. This approach allows for the precise identification of containment and removal areas of Y. gloriosa, without compromising the accuracy of the method. The generated prediction maps also hold potential significance for the conservation of coastal dune ecosystems, providing a promising tool for the effective management of invasive species and biodiversity conservation by suggesting management measures for Y. gloriosa.

Improving embedding-based link prediction performance using clustering

Incomplete knowledge graphs are common problem that can impair task accuracy. As knowledge graphs grow extensively, the probability of incompleteness increases. Link prediction addresses this problem, but accurate and efficient link prediction methods are needed to handle incomplete and extensive knowledge graphs. This study proposed modifications to the embedding-based link prediction using clustering to improve performance. The proposed method involves four main processes: embedding, clustering, determining clusters, and scoring. Embedding converts entities and relations into vectors while clustering groups these vectors. Selected clusters are determined based on the shortest distance between the centroid and the incomplete knowledge graph. Scoring measures relation rankings, and link prediction result is selected based on highest scores. The link prediction performance is evaluated using Hits@1, Mean Rank, Mean Reciprocal Rank and prediction time on three knowledge graph datasets: WN11, WN18RR, and FB13. The link prediction methods used are TransE and ComplEx, with BIRCH as the clustering technique and Mahalanobis for short-distance measurement. The proposed method significantly improves link prediction performance, achieving accuracy up to 98% and reducing prediction time by 99%. This study provides effective and efficient solution for improving link prediction, demonstrating high accuracy and efficiency in handling incomplete and extensive knowledge graphs.

Joint consensus kernel learning and adaptive hypergraph regularization for graph-based clustering

Recent advancements in multiple kernel learning-based graph clustering methods have demonstrated significant promise in effectively learning a consensus kernel matrix from various candidate kernel matrices. This approach enables the creation of a low-dimensional representation in the kernel space of high-dimensional data through self-expressiveness. However, a key challenge remains in capturing the latent geometric properties embedded within different kernel matrices to enhance data representation. In this paper, we propose a novel method called joint consensus kernel learning and adaptive hypergraph regularization for graph-based clustering (JKHR). Our approach integrates an innovative adaptive hypergraph Laplacian regularizer, which is characterized by the fusion of multiple nearest neighbor kernel graphs, into the multiple kernel learning-based graph clustering framework. JKHR jointly and adaptively optimizes both the consensus kernel matrix and the hypergraph Laplacian regularizer to achieve a low-dimensional representation that effectively preserves the intrinsic geometry of the data. Experimental results on both synthetic and real benchmark datasets demonstrate that JKHR outperforms state-of-the-art self-expressiveness-based graph clustering methods as well as traditional clustering techniques.

PopMLvis: a tool for analysis and visualization of population structure using genotype data from genome-wide association studies

One of the aims of population genetics is to identify genetic differences/similarities among individuals of multiple ancestries. Many approaches including principal component analysis, clustering, and maximum likelihood techniques can be used to assign individuals to a given ancestry based on their genetic makeup. Although there are several tools that implement such algorithms, there is a lack of interactive visual platforms to run a variety of algorithms in one place. Therefore, we developed PopMLvis, a platform that offers an interactive environment to visualize genetic similarity data using several algorithms, and generate figures that can be easily integrated into scientific articles.

Recovering nested structures in networks: an evaluation of hierarchical clustering techniques

Abstract In this article, we present various algorithms to partition the nodes of a network into groups that show the property of nestedness. Since perfect nestedness is a rare phenomenon, we consider the task from a data mining perspective, and we search for groups having high-level of nestedness. We utilize both agglomerative and divisive hierarchical clustering procedures and compare them on several benchmark and real-life networks. Furthermore, we propose different metrics derived from the results of our algorithms. We show that average-linkage and complete-linkage clustering can recover the largest fully nested clusters, and that the cluster size-weighted mean nestedness was a more stable metric for measuring clustering performance. Our proposed algorithms allow us to create multiple resolution views of nestedness-based clustering of networks, extending the field of graph-based data mining.