The bereavement response : a cluster analysis

BACKGROUND: Literature and clinical experience suggest that some people experience atypical, complicated or pathological bereavement reactions in response to a major loss. METHOD: Three groups of community-based bereaved subjects--spouses (n = 44), adult children (n = 40), and parents (n = 36)--were followed up four times in the 13 months after a loss. A 17-item scale of core bereavement times was developed and used to investigate the intensity of the bereavement response over time. RESULTS: Cluster analysis revealed a pattern of bereavement-related symptoms approximating a syndrome of chronic grief in 11 (9.2%) of the 120 subjects. None of the respondents displayed a pattern consistent with delayed or absent grief. CONCLUSIONS: In a non-clinical community sample of bereaved people, delayed or absent grief is infrequently seen, unlike chronic grief, which is demonstrated in a minority.

Cluster analysis of movement patterns in multiarticular actions: A tutorial

The present paper proposes a technical analysis method for extracting information about movement patterning in studies of motor control, based on a cluster analysis of movement kinematics. In a tutorial fashion, data from three different experiments are presented to exemplify and validate the technical method. When applied to three different basketball-shooting techniques, the method clearly distinguished between the different patterns. When applied to a cyclical wrist supination-pronation task, the cluster analysis provided the same results as an analysis using the conventional discrete relative phase measure. Finally, when analyzing throwing performance constrained by distance to target, the method grouped movement patterns together according to throwing distance. In conclusion, the proposed technical method provides a valuable tool to improve understanding of coordination and control in different movement models, including multiarticular actions.

Use of hierarchical cluster analysis to assess the representativeness of a baseline groundwater quality monitoring network: comparison of New Zealand’s national and regional groundwater monitoring programs

Baseline monitoring of groundwater quality aims to characterize the ambient condition of the resource and identify spatial or temporal trends. Sites comprising any baseline monitoring network must be selected to provide a representative perspective of groundwater quality across the aquifer(s) of interest. Hierarchical cluster analysis (HCA) has been used as a means of assessing the representativeness of a groundwater quality monitoring network, using example datasets from New Zealand. HCA allows New Zealand's national and regional monitoring networks to be compared in terms of the number of water-quality categories identified in each network, the hydrochemistry at the centroids of these water-quality categories, the proportions of monitoring sites assigned to each water-quality category, and the range of concentrations for each analyte within each water-quality category. Through the HCA approach, the National Groundwater Monitoring Programme (117 sites) is shown to provide a highly representative perspective of groundwater quality across New Zealand, relative to the amalgamated regional monitoring networks operated by 15 different regional authorities (680 sites have sufficient data for inclusion in HCA). This methodology can be applied to evaluate the representativeness of any subset of monitoring sites taken from a larger network.

Segmenting Australian high school students : two-step cluster analysis preliminary results

A one size fits all approach dominates alcohol programs in school settings (Botvin et al., 2007), which may limit program effectiveness (Snyder et al., 2004). Programs tailored to the meet the needs and wants of adolescent groups may be more effective. Limited attention has been directed towards employing a full segmentation process. Where segmentation has been examined, the focus has remained on socio-demographic characteristics and more recently psychographic variables (Mathijssen et al., 2012). The current study aimed to identify whether the addition of behaviour could be used to identify segments. Variables included attitudes towards binge drinking (α = 0.86), behavioral intentions’ (α = 0.97), perceived behavioral control (PBC), injunctive norms (α = 0.94); descriptive norms (α = 0.87), knowledge and reported behaviour. Data was collected from five schools, n = 625 (32.96% girls). Two-Step cluster analysis produced a sample (n = 625) with a silhouette measure of cohesion and separation of 0.4. The intention measure and whether students reported previously consuming alcohol were the most distinguishing characteristics - predictor importance scores of (1.0). A four segment solution emerged. The first segment (“Male abstainers” – 37.2%) featured the highest knowledge score (M: 5.9) along with the lowest-risk drinking attitudes and intentions to drink excessively. Segment 2 (“At risk drinkers” - 11.2%) were characterised by their high-risk attitudes and high-risk drinking intentions. Injunctive (M: 4.1) and descriptive norms (M: 4.9) may indicate a social environment where drinking is the norm. Segment 3 (”Female abstainers” – 25.9%) represents young girls, who have the lowest-risk attitudes and low intentions to drink excessively. The fourth and final segment (boys = 67.4%) (“Moderate drinkers” – 25.7%) all report previously drinking alcohol yet their attitudes and intentions towards excessive alcohol consumption are lower than other segments. Segmentation focuses on identifying groups of individuals who feature similar characteristics. The current study illustrates the importance of including reported behaviour in addition to psychographic and demographic characteristics to identify unique groups to inform intervention planning and design. Key messages The principle of segmentation has received limited attention in the context of school-based alcohol education programs. This research identified four segments amongst 14-16 year high school students, each of which can be targeted with a unique, tailored program to meet the needs and wants of the target audience.

Cluster analysis of rainfall stations of the Indian peninsula

Principal component analysis is applied to derive patterns of temporal variation of the rainfall at fifty-three stations in peninsular India. The location of the stations in the coordinate space determined by the amplitudes of the two leading eigenvectors is used to delineate them into eight clusters. The clusters obtained seem to be stable with respect to variations in the grid of stations used. Stations within any cluster occur in geographically contiguous areas.

Classification of Indian meteorological stations using cluster and fuzzy cluster analysis, and Kohonen artificial neural networks

The present study deals with the application of cluster analysis, Fuzzy Cluster Analysis (FCA) and Kohonen Artificial Neural Networks (KANN) methods for classification of 159 meteorological stations in India into meteorologically homogeneous groups. Eight parameters, namely latitude, longitude, elevation, average temperature, humidity, wind speed, sunshine hours and solar radiation, are considered as the classification criteria for grouping. The optimal number of groups is determined as 14 based on the Davies-Bouldin index approach. It is observed that the FCA approach performed better than the other two methodologies for the present study.

Examining the Effectiveness of Discriminant Function Analysis and Cluster Analysis in Species Identification of Male Field Crickets Based on Their Calling Songs

Traditional taxonomy based on morphology has often failed in accurate species identification owing to the occurrence of cryptic species, which are reproductively isolated but morphologically identical. Molecular data have thus been used to complement morphology in species identification. The sexual advertisement calls in several groups of acoustically communicating animals are species-specific and can thus complement molecular data as non-invasive tools for identification. Several statistical tools and automated identifier algorithms have been used to investigate the efficiency of acoustic signals in species identification. Despite a plethora of such methods, there is a general lack of knowledge regarding the appropriate usage of these methods in specific taxa. In this study, we investigated the performance of two commonly used statistical methods, discriminant function analysis (DFA) and cluster analysis, in identification and classification based on acoustic signals of field cricket species belonging to the subfamily Gryllinae. Using a comparative approach we evaluated the optimal number of species and calling song characteristics for both the methods that lead to most accurate classification and identification. The accuracy of classification using DFA was high and was not affected by the number of taxa used. However, a constraint in using discriminant function analysis is the need for a priori classification of songs. Accuracy of classification using cluster analysis, which does not require a priori knowledge, was maximum for 6-7 taxa and decreased significantly when more than ten taxa were analysed together. We also investigated the efficacy of two novel derived acoustic features in improving the accuracy of identification. Our results show that DFA is a reliable statistical tool for species identification using acoustic signals. Our results also show that cluster analysis of acoustic signals in crickets works effectively for species classification and identification.

Delineation of homogeneous hydrometeorological regions using wavelet-based global fuzzy cluster analysis

Identification of homogeneous hydrometeorological regions (HMRs) is necessary for various applications. Such regions are delineated by various approaches considering rainfall and temperature as two key variables. In conventional approaches, formation of regions is based on principal components (PCs)/statistics/indices determined from time series of the key variables at monthly and seasonal scales. An issue with use of PCs for regionalization is that they have to be extracted from contemporaneous records of hydrometeorological variables. Therefore, delineated regions may not be effective when the available records are limited over contemporaneous time period. A drawback associated with the use of statistics/indices is that they do not provide effective representation of the key variables when the records exhibit non-stationarity. Consequently, the resulting regions may not be effective for the desired purpose. To address these issues, a new approach is proposed in this article. The approach considers information extracted from wavelet transformations of the observed multivariate hydrometeorological time series as the basis for regionalization by global fuzzy c-means clustering procedure. The approach can account for dynamic variability in the time series and its non-stationarity (if any). Effectiveness of the proposed approach in forming HMRs is demonstrated by application to India, as there are no prior attempts to form such regions over the country. Drought severity-area-frequency (SAF) curves are constructed corresponding to each of the newly formed regions for the use in regional drought analysis, by considering standardized precipitation evapotranspiration index (SPEI) as the drought indicator.

Grouping materials and processes for the designer: an application for cluster analysis.

In this paper, an analytical tool - cluster analysis - that is commonly used in biology, archaeology, linguistics and psychology is applied to materials and design. Here we use it to cluster materials and the processes that shape them, using their attributes as indicators of relationship. The attributes that are chosen are important to design and designers. The resulting clusters, and the classifications that can be developed from them, depend on the selected attributes and - to some extent - on the method of clustering. Alternative classifications for design that is focused on the technical or aesthetic attributes of materials and the materials and shapes allowed by processes are explored.

Cluster analysis of heterogeneous rank data

Cluster analysis of ranking data, which occurs in consumer questionnaires, voting forms or other inquiries of preferences, attempts to identify typical groups of rank choices. Empirically measured rankings are often incomplete, i.e. different numbers of filled rank positions cause heterogeneity in the data. We propose a mixture approach for clustering of heterogeneous rank data. Rankings of different lengths can be described and compared by means of a single probabilistic model. A maximum entropy approach avoids hidden assumptions about missing rank positions. Parameter estimators and an efficient EM algorithm for unsupervised inference are derived for the ranking mixture model. Experiments on both synthetic data and real-world data demonstrate significantly improved parameter estimates on heterogeneous data when the incomplete rankings are included in the inference process.

Data analysis with artificial immune systems, cluster analysis and kohonen networks: some comparisons.

Neal M J Timmis J and Hunt J. Data analysis with artificial immune systems, cluster analysis and kohonen networks: some comparisons. In Proceedings of IEEE international conference of systems, man and cybernetics, pages 922-927, Tokyo, 1999. IEEE.

Sequential extracellular matrix-focused and baited-global cluster analysis of serial transcriptomic profiles identifies candidate modulators of renal tubulointerstitial fibrosis in murine adriamycin-induced nephropathy

Transcriptome analysis using microarray technology represents a powerful unbiased approach for delineating pathogenic mechanisms in disease. Here molecular mechanisms of renal tubulointerstitial fibrosis (TIF) were probed by monitoring changes in the renal transcriptome in a glomerular disease-dependent model of TIF ( adriamycin nephropathy) using Affymetrix (mu74av2) microarray coupled with sequential primary biological function-focused and secondary