Mean Shift Research Articles

Understanding non‐stationarity patterns in basin‐scale hydroclimatic extremes

AbstractStationarity, a cornerstone in hydraulic design, is now under scrutiny due to anthropogenic activities and climate change. Numerous studies have sought to identify non‐stationarity (NS); however, a comprehensive assessment of time invariance in all statistical properties of a time series is less explored. This study presents a non‐overlapping block‐stratified random sampling (NBRS) framework leveraging the strengths of several nonparametric tests to assess NS. The NBRS approach exclusively detects NS and distinguishes between various forms of stationarity, including weak and strict. A variant of NBRS is proposed in this study to identify the underlying stochastic process(es) influencing NS in hydroclimatic extremes. Furthermore, a nonparametric clustering approach is used to unveil spatial clusters showcasing NS due to shifts in mean, variance, distribution of time series or a combination of these factors. A comparative assessment of the modified NBRS approach with traditional trend and change point methods is also performed. The proposed methodology is applied to assess the presence of NS in 28 hydroclimatic indices derived for the west‐central river basins of India, exhibiting diverse physio‐climatic settings, for the study period 1973–2021. The modified NBRS approach rigorously explores NS within extreme hydroclimatic indices, conclusively pinpointing its root causes and profound implications for hydrologic design. The applicability of the modified NBRS approach to gridded and point datasets is also demonstrated. The findings highlight the limitations of conventional trend and change point tests in capturing time‐invariant characteristics in heteroscedastic variables (such as streamflow and rainfall extremes) compared to the NBRS approach. The research reveals that NS in rainfall and streamflow extremes primarily results from distributional shifts, whilst temperature extremes are influenced by changes in mean and distribution properties. This research deepens our understanding of the evolving patterns in hydroclimatic extremes in a changing climate.

Optimizing Patient Stratification in Healthcare: A Comparative Analysis of Clustering Algorithms for EHR Data

Advanced data analytics are increasingly being employed in healthcare research to improve patient classification and personalize medicinal therapies. In this paper, we focus on the critical problem of clustering electronic health record (EHR) data to enable appropriate patient categorization. In the era of personalized medicine, optimizing patient classification is critical to healthcare analytics. This research presents a comparative assessment of different clustering algorithms for Electronic Health Record (EHR) data, with the goal of improving the efficacy and productivity of patient clustering methods. Our study focuses on Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (Fuzzy TOPSIS) as a Multi-Criteria Decision-Making (MCDM) strategy, includes an in-depth assessment of eight clustering algorithms: K-Means, DBSCAN, Hierarchical Clustering, Mean Shift, Affinity Propagation, Spectral Clustering, Gaussian Mixture Models (GMM), as well as Self-Organizing Maps. The evaluation factors used for evaluation in this research are Cluster Quality Metrics, Scalability, Robustness to Noise, Cluster Shape and Density, Interpretability, Cluster Number, Dimensionality, and Consistency and Stability. These criteria and alternatives were chosen after conducting a thorough assessment of the literature and consulting with domain experts. All participated specialists actively engaged in the decision-making process, bringing unique insights into the best clustering algorithms for healthcare data. The results of this study illustrate each algorithm’s strengths and weaknesses in the setting of patient stratification, providing insight into their performance across multiple dimensions. The fuzzy TOPSIS MCDM strategy is a reliable instrument for synthesizing expert opinions and methodically evaluating the found clustering alternatives. This study advances healthcare analytics by giving practitioners and researchers with informative perspectives on the selection of clustering algorithms designed to address the unique problems of patient stratification utilizing EHR data.

New extended exponentially weighted moving average control chart for monitoring process mean

In this article, we develop a new control chart based on the Exponentially Weighted Moving Average (EWMA) statistic, termed the New Extended Exponentially Weighted Moving Average (NEEWMA) statistic, designed to recognize slight changes in the process mean. We derive expressions for the mean and variance of the NEEWMA statistic, ensuring an unbiased estimation of the mean, with simulation results showing lower variance compared to traditional EWMA charts. Evaluating its performance using Average Run Length (ARL), our analysis reveals that the NEEWMA control chart outperforms EWMA and Extended EWMA (EEWMA) charts in swiftly recognizing shifts in the process mean. Illustrating its operational methodology through Monte Carlo simulations, an illustrative example using practical data is also provided to showcase its effectiveness.

Measuring data similarity in population-based structural health monitoring using distance metrics

Population-based structural health monitoring (PBSHM) expands structural health monitoring (SHM) from a single structure to a group of structures, allowing inferences to be made within and between populations by transferring knowledge across them. Within the populations of interest, the similarity of structures, via their corresponding data, should be assessed to successfully implement PBSHM. This paper focusses on using distance metrics to assess similarity at the very start of the analysis chain, to discover information about a population for which there is little prior knowledge and before any analysis has taken place on individual structures. By doing so, it is possible to quickly and automatically identify abnormalities within the population, group similarly behaving structures together, and inform further decisions. The suitability of several candidate metrics that are not widely employed in SHM are tested using a number of commonly occurring feature behaviours, such as varying amplitudes and temporary mean shifts. The effect of data normalisation/standardisation on the metrics is also explored to identify interesting behaviours within the data. A case study is then presented where distance metrics are used to discover similarities and dissimilarities within temperature data from turbines in an offshore wind farm.

“We Go Away and Come Back Whenever We Want To”: Place, Time, and Return Migration in Dugi Otok, Croatia

Over the centuries, migration has significantly impacted Croatian islands and islanders. Taking a life course perspective and deploying semistructured interviews, this paper analyses time as a variable in contemporary return migration to Dugi Otok, a small island in Croatia. The focus is on the personal experiences of key events in the interviewees’ individual life histories that influenced their decisions to leave and to return. Comparing with results from an earlier study, the findings challenge traditional notions of a migration cycle in which an initial departure is ‘closed’ by a final, permanent return. A combination of temporally conditioned experiences and shifts in sociotechnical, economic, cultural, and political context mean that emigrants increasingly experience a sense of continuity between island place and immigration destination. Their returns to the island are more likely to be temporary or circular than to be permanent.

Unveiling Multi-Agent Strategies: A Data-Driven Approach for Extracting and Evaluating Team Tactics from Football Event and Freeze-Frame Data

Studying collective behavior in opposing multi-agent teams is crucial across game theory, robotics, and sports analytics. In sports, especially football, team tactics involve intricate strategic spatial and action behaviors displayed as event sequences during possession. Understanding and analyzing these tactics is essential for successful training, strategic planning, and on-field success. While traditional approaches, such as notational and statistical analyses, offer valuable insights into team tactics, they often lack a comprehensive consideration of contextual information, thereby limiting the holistic evaluation of teams’ performances. To bridge this gap and capture the nuanced intricacies of team tactics, we employed advanced methodologies. The sequential pattern mining algorithm PrefixSpan was utilized to extract tactical patterns from possession sequences, enabling a deeper understanding of how teams strategize and adapt during play. Additionally, the neural marked spatio temporal point process (NMSTPP) model was leveraged to model and predict team behaviors, facilitating a fair comparison among teams. The evaluation of team possessions was further enhanced through the innovative holistic possession utilization score metrics, providing a more nuanced assessment of performance. In our experimental exploration, we identified and classified five distinct team tactics, validated the efficacy of the NMSTPP model when integrating StatsBomb 360 data, and conducted a comprehensive analysis of English Premier League teams during the 2022/2023 season. The results were visualized using radar plots and scatter plots with mean shift clustering. Lastly, the potential applications to RoboCup were discussed.

Designing an efficient adaptive EWMA model for normal process with engineering applications

Stability in process parameters is required to ensure the quality of the finished item. Control charts, as one of the critical parts of statistical process monitoring (SPM), have seen widespread use across many disciplines for detecting and responding to shifts in process parameters. The adaptive EWMA (AEWMA) control chartis a well-known scheme that detects both small and large process shifts by integrating the features of the Shewhart and classical EWMA charts. In this study, we propose an enhanced AEWMA chart, termed the EAEWMA chart, that efficiently monitors small and large process mean shifts simultaneously. The proposed EAEWMA chart enhances the existing AEWMA chart using the shift estimator, based on the hybrid EWMA (HEWMA) statistic. The Monte Carlo simulation approach is employed as the computational method to obtain the numerical findings for the various performance metrics. The EAEWMA chart is compared with various existing charts, including AEWMA, HEWMA, EWMA, ACSUM, and IACCUSUM, in zero- and steady-state scenarios. Conclusively, two practical applications of the EAEWMA chart are presented, demonstrating its value for practitioners and engineers and illustrating its efficacy in real-world scenarios.

Comparing the performance of the Cpk and (X‐bar, S) sampling plans

AbstractWhen the Cpk sampling plan is in use, a sample of size (n) is taken from the lot, and the mean and the standard deviation of the sample observations are used to obtain the —the sample estimator of the Cpk index; if the is lower than a threshold (c0), then the lot is rejected, otherwise, the lot is accepted. In recent studies, the α and β risks are the risks of rejecting/accepting lots of items produced by in‐control/out‐of‐control processes with Cpks equal to (, that is, the two parameters (n, c0) of the Cpk sampling plans depend on the inputs (α, , βmax¸ ). When the design of the Cpk sampling plan is based on the inputs C0 and C1, the optimum sample size is always big and, excluding the cases where the magnitude of the mean shift is too small, the β risks associated to the combinations of mean shifts and variance increases (determined by the inputs C0 and C1) are always equal to the maximum allowed value βmax. This fact motivated us to compare the Cpk sampling plan with the (X‐bar, S) sampling plan, where the sample mean (X‐bar) and the sample standard deviation (S) are directly compared with thresholds. For most disturbances, the (X‐bar, S) sampling plan requires smaller samples to meet the condition of β ≤ βmax, that is, for a fixed sample size, it is always possible to find endless combinations of mean shifts and variance increases where the β risks of the (X‐bar, S) sampling plan are β = βmax, and the β risks of the Cpk sampling plan are β > βmax.

Analyzing semantic shifts in English and German by exploring historical influences and societal dynamics

This research examines the mechanisms of semantic change in English and German, focusing on comparing and contrasting these processes across the two languages and investigating the influence of major historical events and societal shifts. By utilizing digital text corpora, libraries, archives, and modern digital media, the study conducts qualitative analyses, including case studies and discourse analysis, to explore semantic evolution. Thematic analysis was employed to thoroughly examine and interpret the data, allowing for the identification of recurring patterns, themes, and significant insights. The results showed that both English and German undergo significant semantic shifts driven by a metaphorical extension (i.e., narrowing or broadening of meanings, pejoration and amelioration, frequency and pathway of change, cultural and historical influences) and historical events and societal shifts that influence semantic change (i.e., wars and conflicts, industrialization, technological advancement, globalization, and cultural exchange, societal movements, and ideological shifts). The role of metaphorical extension is seen in adapting language to technological advancements while highlighting linguistic evolution through narrowing and broadening meanings influenced by cultural, historical, and linguistic factors. Additionally, pejoration and amelioration reflect societal changes and attitudes. Historical events and societal shifts, such as wars, industrialization, technological advancements, globalization, and social movements, significantly impact semantic changes in both English and German. These external influences catalyze language use and meaning shifts, illustrating the interaction between language and human experience.

Improving Accessibility to Outpatient Department Through Reduction of Third Next Available Appointment in Tertiary Hospital in Taif, Saudi Arabia

Background The Outpatient Department (OPD) of Al Hada Armed Forces Hospital - Taif Region (AHAFH) plays a critical role in providing specialized medical services to a diverse population in Taif region, Saudi Arabia. The hospital faces the challenge of ensuring timely access to a comprehensive array of 40 specialized services and subspecialties, supported by 120 rooms in the OPD. This article outlines a significant understanding of the patient flow process and queuing mechanism in the context of the OPD and the dynamics of supply and demand. Local Problem Our project was initiated during the last few weeks of fourth quarter 2020, which revealed a concerning trend of increasing the third next available appointment (TNAA), surpassing the optimal target of <14 days. Methods The study used pre-test and post-test monitoring of the TNAA. Patients’ categories of referral were identified as urgent and routine. We also established a referral and discharge criteria for each clinic specialty and reminder messages on patient appointments 24 hours and 48 hours prior to their actual appointment. Interventions The Plan-Do-Study-Act (PDSA) model for improvement was utilized to implement action plans and interventions to address the increase of TNAA (in days) in the OPD. Weekly huddles were conducted to monitor project progress, action plans, data, and challenges. Results During the intervention phase, notable changes were observed in the control chart, including shifts in TNAA mean decreasing from 9.4 days to 5.6 days after the implementation of four PDSA cycles. Moreover, the average TNAA in each quarter showed consistent reduction in TNAA, with an average of six days in Q1 2022. Conclusions The study sheds light on the complexity of managing the patient flow and access in an outpatient setting. By implementing effective strategies and continuous and vigilant monitoring of the outcomes, outpatient departments can strive to ensure that patients receive specialized care in a timely manner.

Adaptive EWMA control chart by using support vector regression

AbstractTraditional control charts depend on the process parameters that are used to monitor the shifts in the process. The adaptive control charts are used to adapt a process parameter during the online monitoring. This research introduces a support vector regression (SVR) based adaptive exponentially weighted moving average control chat to enhance the monitoring of the mean in industrial processes. The study systematically investigates the comparative efficiency of linear, radial basis function (RBF), and polynomial functions within the SVR framework. The proposed SVR‐based AEWMA control chart leverages the strengths of the RBF kernel, providing a robust mechanism for detecting shifts in the process mean by adapting the smoothing constant according to the size of the shift. To validate the efficacy of the proposed methodology, a practical application is presented by using real‐life data. The application showcases the adaptability and reliability of the SVR‐based adaptive EWMA control chart in effectively monitoring location shifts.

Characterization of mutational signatures demonstrating adaptive mutability post anti-EGFR therapy in a real-world metastatic colorectal cancer cohort.

3545 Background: Clinical benefit from anti-EGFR therapy (EGFRi) for RAS/RAF WT metastatic colorectal cancer (mCRC) is limited by secondary mutations in mitogen-activated protein kinases. The source of these new clones has been debated. In contrast to the outgrowth of pre-existing rare subclones, a new model, termed adaptive mutability, proposes the transient loss of DNA repair genes and concurrent induction of error-prone polymerase repair mechanisms (Russo et al Science, ’19). However, clinical evidence for this effect in a large patient cohort is lacking. Using longitudinal circulating tumor DNA (ctDNA) in a real-world cohort, we sought to explore mutational signatures and assess the presence of adaptive mutability in mCRC patients after EGFRi treatment. Methods: 288 patients from Guardant Health’s GuardantINFORM database with mCRC who received EGFRi and underwent longitudinal ctDNA NGS testing by Guardant360 (Redwood City, CA) between December 2014 and March 2023 were included. We extracted de novo single-nucleotide variant mutational signatures from the somatic ctDNA mutations per-cohort level, comparing pre- to post-treatment mutational profiles. Extracted mutational signatures were compared against COSMIC catalogue. Results: Of the 288 patients identified, 99.7% (287/288) were MSS/MSI-L prior to treatment. Pre-treatment, patients had a mean and median of 3 reportable variants per sample, with 30.2% (87/288) samples with >5 variants. Post-treatment, patients had a mean of 5 and a median of 4 reportable variants, with 64.6% (186/288) of patients with >5 variants. Overall mean shift in reportable variants between pre- and post-EGFRi was 5 variants, with a median of 4 changes. In comparing pre- and post-EGFRi cohorts for new mutations, three mutational signatures emerged: SBS17b, SBS15, and SBS3. SBS15 is associated with mismatch repair (MMR) deficiency and classically seen in MSI-H tumors, though notably this cohort was overwhelmingly MSS. SBS15 has been associated with polymerase epsilon exonuclease (POLE) defects and likely reflects repair by alternate error-prone polymerases such as Polι, Polλ, and Polκ. SBS3 has been associated with homologous recombination (HR) deficiency. SBS17b has been associated with exposure to 5-FU based chemotherapy in CRC patients, which was co-administered in many of the EGFRi treated patients. Conclusions: New mutations arising with EGFR inhibition demonstrate strong evidence for adaptive mutability, showcasing loss of mismatch repair and HR fidelity, and reliance on error-prone polymerases. This is the first large cohort to indirectly demonstrate evidence for this genomic mechanism underlying secondary acquired resistance. Further validation of the adaptive mutability signatures may support therapeutic strategies leveraging this vulnerability to extend benefit from targeted therapies.

Asymptotic behavior of a generalized independent sets model on the two-dimensional Sierpinski gasket

We consider a generalized independent set model on the two-dimensional Sierpinski gasket SGn. Let A be a set of symbols and each vertex of SGn should take a symbol from A. Given a subset S⊂A such that the symbols of nearest-neighbor vertices are not allowed to be the forbidden blocks F={ij:i,j∈S}. The probability of each vertex of SGn to take a symbol in S is denoted by p ∈ (0, 1), and the probability of each vertex to take a symbol not in S is given by 1 − p. When A={0,1}, S={0}, and p = 1/2, this reduces to independent set model or golden mean shift in symbolic dynamics. We investigate the asymptotic behavior of this generalized model on the two-dimensional Sierpinski gasket and obtain upper and lower bounds of the entropy per site for p ∈ (0, 1).

Mapping emergent coral reefs: a comparison of pixel‐ and object‐based methods

AbstractAlthough emergent coral reefs represent a significant proportion of overall reef habitat, they are often excluded from monitoring projects due to their shallow and exposed setting that makes them challenging to access. Using drones to survey emergent reefs overcomes issues around access to this habitat type; however, methods for deriving robust monitoring metrics, such as coral cover, are not well developed for drone imagery. To address this knowledge gap, we compare the effectiveness of two remote sensing methods in quantifying broad substrate groups, such as coral cover, on a lagoon bommie, namely a pixel‐based (PB) model versus an object‐based (OB) model. For the OB model, two segmentation methods were considered: an optimized mean shift segmentation and the fully automated Segment Anything Model (SAM). Mean shift segmentation was assessed as the preferred method and applied in the final OB model (SAM exhibited poor identification of coral patches on the bommie). While good cross‐validation accuracies were achieved for both models, the PB had generally higher overall accuracy (mean accuracy PB = 75%, OB = 70%) and kappa (mean kappa PB = 0.69, OB = 0.63), making it the preferred method for monitoring coral cover. Both models were limited by the low contrast between Coral features and the bommie substrate in the drone imagery, causing indistinct segment boundaries in the OB model that increased misclassification. For both models, the inclusion of a drone‐derived digital surface model and multiscale derivatives was critical to predicting coral habitat. Our success in creating emergent reef habitat models with high accuracy demonstrates the niche role drones could play in monitoring these habitat types, which are particularly vulnerable to rising sea surface and air temperatures, as well as sea level rise which is predicted to outpace reef vertical accretion rates.

Comparison of the Direct Supervised Classification and Segment Mean Shift Classification: A Case Study of Hetauda Sub-Metropolitan City of Nepal

Comparison between Land Use Land Cover (LULC) classification and methods is a common practice to assess their suitability and reliability in specific areas. In the case of Hetauda Sub-Metropolitan City in Nepal, Sentinel-2B satellite imagery was utilized to evaluate the accuracy of LULC classification through both direct supervised classification and segment mean shift classification. Direct supervised classification was conducted in ERDAS Imagine, while segment mean shift classification was performed in ArcGIS to identify six LULC classes: forest, agriculture, built-up area, bare area, grassland, and waterbody. Pre-processing steps such as layer stacking, mosaicking, data extraction, atmospheric and radiometric correction were employed to enhance the imagery. Training samples were chosen via visual interpretation based on local knowledge to train the Maximum Likelihood Classifier (MLC). The results revealed that the segment mean shift method achieved the highest overall accuracy at 90%, whereas direct supervised classification yielded 85%. Furthermore, kappa statistics indicated a strong level of agreement, with segment mean shift scoring the highest at 0.88 and direct supervised classification at 0.81. Individual class accuracies varied, with forest classification being the most reliable at 98% and waterbody classification at 71%. Overall, the segment mean shift method was deemed more accurate and suitable than the pixel-based direct supervised classification for LULC classification in Hetauda Sub-Metropolitan City, Nepal.

Optimal design of the side sensitive group runs double sampling (SSGRDS) X¯ scheme with estimated process parameters

The side sensitive group runs double sampling (SSGRDS) [Formula: see text] scheme is previously studied under the known process parameters (Case-K) assumption. As the process parameters are hardly known in practical situations, they must be estimated using a suitably chosen in-control (IC) Phase-I sample. However, various studies demonstrated that a substantial amount of Phase-I sample is necessary so that the scheme with unknown process parameters (Case-U) attains a performance almost similar to that of the Case-K counterpart. Since it is challenging to acquire a vast number of IC samples, we examine the optimal designs of the Case-U SSGRDS [Formula: see text] scheme such that the average number of observations to signal (ANOS) and expected ANOS (EANOS) are minimised. The optimal parameters obtained for the Case-U SSGRDS [Formula: see text] scheme enable it to have a performance comparable to that of the Case-K counterpart, without the need for a large number of Phase-I samples. We show that the Case-U SSGRDS [Formula: see text] scheme is effective in identifying the process mean shift of a silicon epitaxial process.

A nonparametric adaptive EWMA control chart for monitoring multivariate time-between-events-and-amplitude data

Multivariate time-between-events-and-amplitude (TBEA) data, which includes the time interval between two consecutive occurrences of the event as well as their amplitudes, is a common data type in manufacturing and service operations. Designing control charts to monitor TBEA data is of great significance to improve product quality. However, most TBEA control charts are typically constructed under the assumption that the data distribution is known. The monitoring performance of these parametric TBEA control charts may become less reliable when the assumed data distribution is unknown. To address this issue, we propose a nonparametric adaptive TBEA (NATBEA) control chart based on the antirank method and adaptive strategy. Firstly, a nonparametric monitoring statistic is constructed by using the antirank method. With using the adaptive strategy, a nonparametric adaptive EWMA control chart is designed to monitor TBEA data, and the monitoring performance of various shifts is improved. Then, we compare the monitoring performance of the NATBEA control chart and other eight control charts, and the numerical simulation results show that the NATBEA control chart has superior monitoring performance for various mean shifts with different data distributions. Finally, an example of a company’s production failure demonstrates that the developed NATBEA control chart outperforms various conventional control charts by providing rapid alarms when the process is out of control.

An unsupervised method for rock discontinuities rapid characterization from 3D point clouds under noise

The presence of rock discontinuities significantly affects the strength, permeability, and stability of rock masses, so it is crucial to characterize them accurately and efficiently. However, existing discontinuity characterization methods mainly focus on improving automation level and computational efficiency and pay little attention to the effect of noise, which may lead to unsatisfactory results. To address this, we present an unsupervised method to characterize rock discontinuities. The method contains five denoising operations which greatly mitigate the effects of noise while mapping discontinuities rapidly. The proposed approach comprises four steps: (1) remote sensing technology used to collect point cloud data; (2) a Modified Mean Shift algorithm (MMS) including five denoising operations developed to identify group discontinuity; (3) a density-based cluster algorithm employed to detect individual discontinuity; and (4) plane fitting algorithm applied to calculate the orientation of each discontinuity. The working procedure was displayed using a regular cube, and the proposed approach was validated on two real road-cut slopes. The average error degrees in dip angle and dip direction are within 3° for both cases, indicating the excellent accuracy of the presented method. Then, a sensitivity analysis of the parameters in the MMS was conducted, and some recommendations for the selection of these parameters were given (ηmax was suggested to be 0.02–0.2, while R was recommended to be 0.2–0.3). Finally, the computational efficiency of the presented method was compared with the other two algorithms, and the results show that the computational efficiency was improved by 13 and 30 times respectively, which proves that it is computationally efficient. This approach gives geological and geotechnical engineers a new solution to rapidly map rock structures under noise, which is significant for improving the accuracy of rock characterization.

Salvation, Enlightenment and Awakening in Shaping Followers' Perception and Understanding of Their Faith: A Linguistic Exploration

This paper examines how religious concepts of salvation, enlightenment, and awakening shape followers’ perception and understanding of their faith. Drawing on theories of language ideology (Woolard, 1998) and linguistic structuration (Giddens, 1979), it explores how linguistic and conceptual variations surrounding these key terms influence religious worldviews. A mixed methods approach integrates comparative-historical case studies, interviews, surveys, and textual analysis of three religious’ denominations. Findings reveal ideological divergences in the metaphorical framing of the key concepts, relating to differences in perceived resonances and definitions among followers. The study demonstrates that religious conceptual systems transmit between contexts via semiotic processes of indexicality and erasure, undergoing shifts in meaning. These ideological variations in turn contribute to divergent understandings of core principles across faith traditions. Ultimately, illuminating conceptual divergences and the symbolic processes by which they spread provides vital insight into religion’s cultural logic and diversity. The research contributes to theoretical models of religious language’s co-constitutive relationship with thought and experience. It offers an empirical approach employing cognitive linguistics and the sociology of religion to promote understanding across belief systems in an increasingly pluralistic world.

High-Dimensional Data Analysis Using Parameter Free Algorithm Data Point Positioning Analysis

Clustering is an effective statistical data analysis technique; it has several applications, including data mining, pattern recognition, image analysis, bioinformatics, and machine learning. Clustering helps to partition data into groups of objects with distinct characteristics. Most of the methods for clustering use manually selected parameters to find the clusters from the dataset. Consequently, it can be very challenging and time-consuming to extract the optimal parameters for clustering a dataset. Moreover, some clustering methods are inadequate for locating clusters in high-dimensional data. To address these concerns systematically, this paper introduces a novel selection-free clustering technique named data point positioning analysis (DPPA). The proposed method is straightforward since it calculates 1-NN and Max-NN by analyzing the data point placements without the requirement of an initial manual parameter assignment. This method is validated using two well-known publicly available datasets used in several clustering algorithms. To compare the performance of the proposed method, this study also investigated four popular clustering algorithms (DBSCAN, affinity propagation, Mean Shift, and K-means), where the proposed method provides higher performance in finding the cluster without using any manually selected parameters. The experimental finding demonstrated that the proposed DPPA algorithm is less time-consuming compared to the existing traditional methods and achieves higher performance without using any manually selected parameters.