The paper explores the effectiveness of automated clustering in personalized applications based on data characteristics. It evaluates three clustering algorithms with various cluster numbers and subsets of characteristics. The study compares the accuracy of models in different clusters against original results and examines the algorithmic approaches and characteristic selections for optimal clustering performance. The research concludes that the proposed method aids in selecting appropriate clustering strategies and relevant characteristics for datasets. These insights may also guide further research on coaching approaches within applications.
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Insufficient physical activity presents a significant hazard to overall health, with sedentary lifestyles linked to a variety of health issues. Monitoring physical activity levels allows the recognition of patterns of sedentary behavior and the provision of coaching to meet the recommended physical activity standards. In this paper, we aim to address the problem of reducing the time consuming process of fitting classifiers when generating personalized models for a coaching application. The proposed approach consists of evaluating the effects of clustering participants based on their walking patterns and then recommending a unique model for each group. Each model consists of a random forest classifier with a different number of estimators each. The resulting approach reduces the fitting time considerably while keeping nearly the same classification performance as personalized models.
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Purpose: The purpose of this study is to assess the evolution of restaurant locations in the city of Hamilton over a 12-year period (1996 to 2008) using GIS techniques. Retail theories such as central place, spatial interaction and principle of minimum differentiation are applied to the restaurant setting. Design/methodology/approach: A database of restaurants was compiled using the NZ yellow pages and contained 981 entries that consisted mainly of location addresses and types of cuisine. This paper focuses on locational patterns only. Findings: A process of geo-coding and clustering enabled the identification of two clustering periods over 12 years for city restaurants, indicating locational patterns of agglomeration within a short walking distance of the CBD and spill over effects to the north of the city. Research limitations/implications: The data do not allow statistical analysis of the variables causing the clustering but offer a visual description of the evolution. Explanations are offered on the possible planning regimes, retail provision and population changes that may explain this evolution. Practical implications: The findings allow identification of land use patterns in Hamilton city and potential areas where new restaurants could be developed. Also, the usefulness of geo-coded data in identifying clustering effects is highlighted. Originality/value: Existing location studies relate mostly to site selection criteria in the retailing industry while few have considered the evolution of restaurant locations in a specific geographic area. This paper offers a case study of Hamilton city and highlights the usefulness of GIS techniques in understanding locational patterns.
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The objective of this study was to generate groups of agri-food producers with high affinity in relation to their sustainable waste management practices. The aim of conforming these groups is the development of synergies, knowledge management, and policy- and decision-making by diverse stakeholders. A survey was conducted among the most experienced farmers in the region of Nuevo Urecho, Michoacán, Mexico, and a total of eight variables relating to sustainable waste management practices, agricultural food loss, and the waste generated at each stage of the production process were examined. The retrieved data were treated using the maximum inverse correspondence algorithm and the Galois Lattice was applied to generate clusters of highly affine producers. The results indicate 163 possible elements that generate the power set, and 31 maximum inverse correspondences were obtained. At this point, it is possible to determine the maximum number of relationships, called affinities. In general, all 15 considered farmers shared the measure of revaluation of food waste and 90% of the farmers shared affinity in measures related to ecological care and the proper management of waste. A practical implication of this study is the conformation of highly affine clusters for both policy and strategic decision-making.
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There has been a rapidly growing number of studies of the geographical aspects of happiness and well-being. Many of these studies have been highlighting the role of space and place and of individual and spatial contextual determinants of happiness. However, most of the studies to date do not explicitly consider spatial clustering and possible spatial spillover effects of happiness and well-being. The few studies that do consider spatial clustering and spillovers conduct the analysis at a relatively coarse geographical scale of country or region. This article analyses such effects at a much smaller geographical unit: community areas. These are small area level geographies at the intra-urban level. In particular, the article presents a spatial econometric approach to the analysis of life satisfaction data aggregated to 1,215 communities in Canada and examines spatial clustering and spatial spillovers. Communities are suitable given that they form a small geographical reference point for households. We find that communities’ life satisfaction is spatially clustered while regression results show that it is associated to the life satisfaction of neighbouring communities as well as to the latter's average household income and unemployment rate. We consider the role of shared cultural traits and institutions that may explain such spillovers of life satisfaction. The findings highlight the importance of neighbouring characteristics when discussing policies to improve the well-being of a (small area) place.
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Summary:A novel Smart Charging strategy, based on low base allowances per charger combined with 1. clustering of chargers on the same part of the grid and 2. dynamic non guaranteed allowance, is presented in this paper. This manner of Smart Charging will allow more than 3 times the amount of chargers to be installed in the existing grid, even when the grid is already congested. The system also improves the usage of available flexibility in EV charging compared to other Smart Charging strategies. The required algorithms are tested on public chargers in Amsterdam, in some of the most intensely used parts of the Dutch grid.
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The aim of the present study was to investigate if the presence of anterior cruciate ligament (ACL) injury risk factors depicted in the laboratory would reflect at-risk patterns in football-specific field data. Twenty-four female footballers (14.9 ± 0.9 year) performed unanticipated cutting maneuvers in a laboratory setting and on the football pitch during football-specific exercises (F-EX) and games (F-GAME). Knee joint moments were collected in the laboratory and grouped using hierarchical agglomerative clustering. The clusters were used to investigate the kinematics collected on field through wearable sensors. Three clusters emerged: Cluster 1 presented the lowest knee moments; Cluster 2 presented high knee extension but low knee abduction and rotation moments; Cluster 3 presented the highest knee abduction, extension, and external rotation moments. In F-EX, greater knee abduction angles were found in Cluster 2 and 3 compared to Cluster 1 (p = 0.007). Cluster 2 showed the lowest knee and hip flexion angles (p < 0.013). Cluster 3 showed the greatest hip external rotation angles (p = 0.006). In F-GAME, Cluster 3 presented the greatest knee external rotation and lowest knee flexion angles (p = 0.003). Clinically relevant differences towards ACL injury identified in the laboratory reflected at-risk patterns only in part when cutting on the field: in the field, low-risk players exhibited similar kinematic patterns as the high-risk players. Therefore, in-lab injury risk screening may lack ecological validity.
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The purpose of this study was to provide more insight into how the physical education (PE) context can be better tailored to the diverse motivational demands of secondary school students. Therefore, we examined how different constructs of student motivation in the context of PE combine into distinct motivational profiles, aiming to unveil motivational similarities and differences between students’ PE experiences. Participants were 2,562 Dutch secondary school students, aged 12–18, from 24 different schools. Students responded to questionnaires assessing their perception of psychological need satisfaction and frustration, and perceived mastery and performance climate in PE. In order to interpret the emerging profiles additional variables were assessed (i.e. demographic, motivational and PE-related variables). Two-step cluster analysis identified three meaningful profiles labelled as negative perceivers, moderate perceivers and positive perceivers. These three profiles differed significantly with regard to perceived psychological need satisfaction and frustration and their perception of the motivational climate. This study demonstrates that students can be grouped in distinct profiles based on their perceptions of the motivational PE environment. Consequently, the insights obtained could assist PE teachers in designing instructional strategies that target students’ differential motivational needs.
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Preprint submitted to Information Processing & Management Tags are a convenient way to label resources on the web. An interesting question is whether one can determine the semantic meaning of tags in the absence of some predefined formal structure like a thesaurus. Many authors have used the usage data for tags to find their emergent semantics. Here, we argue that the semantics of tags can be captured by comparing the contexts in which tags appear. We give an approach to operationalizing this idea by defining what we call paradigmatic similarity: computing co-occurrence distributions of tags with tags in the same context, and comparing tags using information theoretic similarity measures of these distributions, mostly the Jensen-Shannon divergence. In experiments with three different tagged data collections we study its behavior and compare it to other distance measures. For some tasks, like terminology mapping or clustering, the paradigmatic similarity seems to give better results than similarity measures based on the co-occurrence of the documents or other resources that the tags are associated to. We argue that paradigmatic similarity, is superior to other distance measures, if agreement on topics (as opposed to style, register or language etc.), is the most important criterion, and the main differences between the tagged elements in the data set correspond to different topics
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Background: Osteoarthritis is one of the most common chronic joint diseases, mostly affecting the knee or hip through pain, joint stiffness and decreased physical functioning in daily life. Regular physical activity (PA) can help preserve and improve physical functioning and reduce pain in patients with osteoarthritis. Interventions aiming to improve movement behaviour can be optimized by tailoring them to a patients' starting point; their current movement behaviour. Movement behaviour needs to be assessed in its full complexity, and therefore a multidimensional description is needed. Objectives: The aim of this study was to identify subgroups based on movement behaviour patterns in patients with hip and/or knee osteoarthritis who are eligible for a PA intervention. Second, differences between subgroups regarding Body Mass Index, sex, age, physical functioning, comorbidities, fatigue and pain were determined between subgroups. Methods: Baseline data of the clinical trial 'e-Exercise Osteoarthritis', collected in Dutch primary care physical therapy practices were analysed. Movement behaviour was assessed with ActiGraph GT3X and GT3X+ accelerometers. Groups with similar patterns were identified using a hierarchical cluster analysis, including six clustering variables indicating total time in and distribution of PA and sedentary behaviours. Differences in clinical characteristics between groups were assessed via Kruskall Wallis and Chi2 tests. Results: Accelerometer data, including all daily activities during 3 to 5 subsequent days, of 182 patients (average age 63 years) with hip and/or knee osteoarthritis were analysed. Four patterns were identified: inactive & sedentary, prolonged sedentary, light active and active. Physical functioning was less impaired in the group with the active pattern compared to the inactive & sedentary pattern. The group with the prolonged sedentary pattern experienced lower levels of pain and fatigue and higher levels of physical functioning compared to the light active and compared to the inactive & sedentary. Conclusions: Four subgroups with substantially different movement behaviour patterns and clinical characteristics can be identified in patients with osteoarthritis of the hip and/or knee. Knowledge about these subgroups can be used to personalize future movement behaviour interventions for this population.
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