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PubMed1984

Study of sleep-wakefulness states by computer graphics and cluster analysis before and after lesions of the pontine tegmentum in the cat.

A computerized method of quantification, graphic representation and classification of sleep-wakefulness data in the cat before and after pontine tegmental lesions has been presented. Electrophysiological signal features including average EEG amplitude, average EMG amplitude and PGO spike rate which are particularly important for the definition of sleep-wakefulness states have been quantified for each 1 min epoch in the day. The data were presented in a projected 3-dimensional data display, in which they formed clusters that are considered to be analogous to sleep-wakefulness states. A cluster analysis algorithm was employed for the automatic classification of these data, and this automatic classification was compared graphically and with contingency table analyses to traditional visual assessment of state from polygraphic records. Although there were systematic differences in the locations of state boundaries, total percent agreement between cluster analysis classification and traditional human classification was comparable to the percent agreement between any two human classifiers (about 90%). After pontine tegmental lesions involving both the gigantocellular and lateral tegmental fields, paradoxical sleep was eliminated, and the characteristics of slow wave sleep and wakefulness were altered. The elimination of the state of paradoxical sleep was evident in the computer display by the absence of the paradoxical sleep cluster, and alterations of the other states were indicated in the display by shifts in the positions of their respective clusters. Automatic classification of slow wave sleep and wakefulness after such lesions compared well with traditional classification, attesting to the validity of this approach.

Animals↗
PubMed2000

Mining of biological data II: assessing data structure and class homogeneity by cluster analysis.

An important step in data analysis is class assignment which is usually done on the basis of a macroscopic phenotypic or bioprocess characteristic, such as high vs low growth, healthy vs diseased state, or high vs. low productivity. Unfortunately, such an assignment may lump together samples, which when derived from a more detailed phenotypic or bioprocess description are dissimilar, giving rise to models of lower quality and predictive power. In this paper we present a clustering algorithm for data preprocessing which involves the identification of fundamentally similar lots on the basis of the extent of similarity among the system variables. The algorithm combines aspects of cluster analysis and principal component analysis by applying agglomerative clustering methods to the first principal component of the system data matrix. As part of a rational strategy for developing empirical models, this technique selects lots (samples) which are most appropriate for inclusion in a training set by analyzing multivariate data homogeneity. Samples with similar data structures are identified and grouped together into distinct clusters. This knowledge is used in the formation of potential training sets. Additionally, this technique can identify atypical lots, i.e., samples that are not simply outliers but exhibit the general properties of one class but have been given the assignment of the other. The method is presented along with examples from its application to fermentation data sets.

Algorithms↗
PubMedDate not supplied

Bayesian model based clustering analysis: application to a molecular dynamics trajectory of the HIV-1 integrase catalytic core.

This work describes the application of a Bayesian method for clustering protein conformations sampled during a molecular dynamics simulation of the HIV-1 integrase catalytic core. A clustering analysis is carried out under the assumption of normal distribution without fixing the number of clusters in advance. Some performance measures, such as posterior probability and class cross entropy, are used to determine the most probable set of clusters. The Bayesian clustering method results in meaningful groups identifying transitions between conformational ensembles. The dihedral angles involved in such transitions are also examined in detail. The conformations in high dimensional space are projected into 3D space employing a multidimensional scaling technique to provide a visual inspection.

Algorithms↗
PubMed2004

Personality typologies of male juvenile offenders using a cluster analysis of the Millon Adolescent Clinical Inventory introduction.

The Millon Adolescent Clinical Inventory (MACI) is a unique adolescent instrument that attempts to delineate between personality and acute symptoms. This study sought to explore typologies based on the Personality Pattern scales of the MACI in a sample of detained male juvenile offenders (N = 103). A Ward's method cluster analysis yielded a four-cluster solution, and each cluster was provided a clinically relevant label: (a) disruptive, antisocials; (b) agreeable, antisocials; (c) anxious, prosocials; and (d) reactive, depressives. The largest group consisted of the reactive depressives (n = 41). This suggests the importance of considering the role of internalizing problems as a conduit to delinquency in addition to antisocial personality. No interaction between cluster membership and offense history or race was found.

Adolescent↗
PubMed1999

Complexities in complex posttraumatic stress disorder in inpatient women: evidence from cluster analysis of MCMI-III Personality Disorder Scales.

Herman's (1992a) clinical formulation of complex posttraumatic stress disorder (PTSD) captures the extensive diagnostic comorbidity seen in patients with a history of repeated interpersonal trauma and severe psychiatric disorders. Yet the sheer breadth of symptoms and personality disturbance encompassed by complex PTSD limits its descriptive usefulness. This study employed cluster analysis of the MCMI-III (Millon, 1994) personality disorder scales to determine whether there is meaningful heterogeneity within a group of 227 severely traumatized women who were treated in a specialized inpatient program. The analysis distinguishes 5 clinically meaningful clusters, which we label alienated, withdrawn, aggressive, suffering, and adaptive. The study examined differences among these 5 personality disorder clusters on the MCMI-III clinical syndrome scales, as well as on the Brief Symptom Inventory (Derogatis, 1993), Dissociative Experiences Scale (E. M. Bernstein & Putnam, 1986), Adult Attachment Scale (Collins & Read, 1990), and Childhood Trauma Questionnaire (D.P. Bernstein, 1995). We present a classification-tree method for determining the cluster membership of new cases and discuss the implications of the findings for diagnostic assessment, treatment, and research.

Adult↗
PubMed1983

Discriminating borderline disorder from other personality disorders. Cluster analysis of the diagnostic interview for borderlines.

The statistical technique of cluster analysis was applied to 252 hospitalized patients' scores on the 29 statements of the Diagnostic Interview for Borderlines. We found that this statistical treatment could reliably differentiate borderline disorder from other personality disorders. Two subtypes similar to Spitzer's schizotypal and unstable subtypes emerged.

Borderline Personality Disorder↗
PubMed2000

Food patterns defined by cluster analysis and their utility as dietary exposure variables: a report from the Malmö Diet and Cancer Study.

OBJECTIVE: To explore the utility of cluster analysis in defining complex dietary exposures, separately with two types of variables. DESIGN: : A modified diet history method, combining a 7-day menu book and a 168-item questionnaire, assessed dietary habits. A standardized questionnaire collected information on sociodemographics, lifestyle and health history. Anthropometric information was obtained through direct measurements. The dietary information was collapsed into 43 generic food groups, and converted into variables indicating the per cent contribution of specific food groups to total energy intake. Food patterns were identified by the QUICK CLUSTER procedure in SPSS, in two separate analytical steps using unstandardized and standardized (Z-scores) clustering variables. SETTING: : The Malmö Diet and Cancer (MDC) Study, a prospective study in the third largest city of Sweden, with baseline examinations from March 1991 to October 1996. SUBJECTS: A random sample of 2206 men and 3151 women from the MDC cohort (n = 28 098). RESULTS: Both variable types produced conceptually well separated clusters, confirmed with discriminant analysis. 'Healthy' and 'less healthy' food patterns were also identified with both types of variables. However, nutrient intake differences across clusters were greater, and the distribution of the number of individuals more even, with the unstandardized variables. Logistic regression indicated higher risks of past food habit change, underreporting of energy and higher body mass index (BMI) for individuals falling into 'healthy' food pattern clusters. CONCLUSIONS: The utility in discriminating dietary exposures appears greater for unstandardized food group variables. Future studies on diet and cancer need to recognize the confounding factors associated with 'healthy' food patterns.

Aged↗
PubMedDate not supplied

Cluster analysis of psychogeriatric characteristics and service use among rural elders.

In developing models of psychiatric service delivery, nurses must be able to target groups on the basis of their health status and service needs. This investigation attempted to develop profiles of rural elderly, a significant risk population, by subjecting data on the psychogeriatric nursing status and health services utilization of 125 subjects to cluster-analytic methods. The cluster analysis yielded a three-cluster model: Cluster 1 (n = 39) predominantly comprised unmarried women in moderate health, but with a high degree of health service utilization; Cluster 2 (n = 53) had rural elders with moderate physical impairments, self-perceptions of poor health, and moderate health service utilization; and Cluster 3 (n = 33) comprised elders with severe cognitive and physical impairments and high health service utilization. Cluster 2 subjects were judged to be mild users of services because they were younger and married without a regular source of health care. Because subjects in Cluster 1 tended to be unmarried women who lived alone, with mild to moderate physical impairments and a regular source of health care, these subjects were assessed as moderate users of services. Cluster 3, which comprised the oldest and most impaired, both physically and cognitively, were judged to be intensive users of services.

Activities of Daily Living↗
PubMed1981

Neuropsychology and cluster analysis: potentials and problems.

This report presents a selective overview of the cluster analysis literature and its potential uses in neuropsychology. In addition, an actual problem involving data from the Florida Longitudinal Project is presented to provide a practical example of many of the processes and problems involved in cluster analytic techniques. It is hoped that the reader will gain a theoretical and practical understanding of such methods and their potential usefulness in neuropsychology and other related areas.

Child↗
PubMed1990

Hydrophobic cluster analysis: procedures to derive structural and functional information from 2-D-representation of protein sequences.

Hydrophobic cluster analysis (HCA) [15] is a very efficient method to analyse and compare protein sequences. Despite its effectiveness, this method is not widely used because it relies in part on the experience and training of the user. In this article, detailed guidelines as to the use of HCA are presented and include discussions on: the definition of the hydrophobic clusters and their relationships with secondary and tertiary structures; the length of the clusters; the amino acid classification used for HCA; the HCA plot programs; and the working strategies. Various procedures for the analysis of a single sequence are presented: structural segmentation, structural domains and secondary structure evaluation. Like most sequence analysis methods, HCA is more efficient when several homologous sequences are compared. Procedures for the detection and alignment of distantly related proteins by HCA are described through several published examples along with 2 previously unreported cases: the beta-glucosidase from Ruminococcus albus is clearly related to the beta-glucosidases from Clostridum thermocellum and Hansenula anomala although they display a reverse organization of their constitutive domains; the alignment of the sequence of human GTPase activating protein with that of the Crk oncogene is presented. Finally, the pertinence of HCA in the identification of important residues for structure/function as well as in the preparation of homology modelling is discussed.

Amino Acid Sequence↗
PubMed2005

Proposal of a comprehensive clinical typology of alcohol withdrawal--a cluster analysis approach.

AIMS: To characterize the various courses of alcohol withdrawal. METHODS: The Alcohol Withdrawal Scale (AWS) was applied to 217 alcohol-dependent patients every 4 h till the symptoms of withdrawal had passed (until each of four consecutive scores were <3). Patients were medicated by a standardized treatment scheme according to AWS-scores. Hierarchical cluster analysis and discriminant analysis were applied. RESULTS: We found five clusters representing increasing severity of alcohol withdrawal. Each cluster is characterized by a combination of the two maximum subscores (vegetative and psychopathological subscore) and three additional psychopathological symptoms (anxiety, disorientation, and hallucination). In 18.4% of the patients, relevant symptoms were not observed (cluster 1), 18.9% developed mild or moderate vegetative symptoms only (cluster 2), and 40.6% additional anxiety (cluster 3). In cluster 4 (11.1%) the most frequent psychopathological symptoms were disorientation and anxiety but no hallucinations, which could be observed only in cluster 5 (11.1%). Discriminant analysis using the maximum subscores at the first day of treatment as independent variables correctly predicted 89.9% of the five clusters. CONCLUSIONS: Our findings support a model of alcohol withdrawal clustering along the two dimensions of vegetative and psychopathological severity. Furthermore, the AWS may be useful to predict the course of alcohol withdrawal already at the first day of treatment.

Adult↗
PubMed1996

Study of chronic lymphocytic leukemia cells by FT-IR spectroscopy and cluster analysis.

The peripheral mononuclear cells from 23 normal individuals and the purified B cells from 38 patients with chronic lymphocytic leukemia (CLL) were examined by Fourier transform infrared (FT-IR) spectroscopy. Differences were observed between the CLL and normal cells at the DNA, protein and lipid levels, with CLL cells having greater DNA and lower lipid contents than normal cells. In addition, the spectral character of the CLL and normal cells varied, demonstrating that there were also qualitative differences in the DNA and lipids. Statistical analysis, based on hierarchical clustering, separated normal from CLL cells completely and classified them into two subgroups for normal cells, while the CLL cells could be divided into three subgroups that were distinct from the normal cells. These differences were based on the lipid and DNA content and the overall spectral character of the cells.

B-Lymphocytes↗
PubMed2005

A cluster analysis of bacterial vaginosis-associated microflora and pelvic inflammatory disease.

Controversy surrounds the association between bacterial vaginosis (BV) and pelvic inflammatory disease (PID). Women (N = 1,140) were ascertained at five US centers, enrolled (1999-2001), and followed up for a median of 3 years. Serial vaginal swabs were obtained for Gram's stain and cultures. PID was defined as 1) histologic endometritis or 2) pelvic pain and tenderness plus oral temperature >38.8 degrees C, leukorrhea or mucopus, erythrocyte sedimentation rate >15 mm/hour, white blood cell count >10,000, or gonococcal/chlamydial lower genital infection. Exploratory factor analysis identified two discrete clusters of genital microorganisms. The first correlated with BV by Gram's stain and consisted of the absence of hydrogen peroxide-producing lactobacillus, Gardnerella vaginalis, Mycoplasma hominis, anaerobic gram-negative rods, and, to a lesser degree, Ureaplasma urealyticum. The second, unrelated to BV by Gram's stain, consisted of Enterococcus species and Escherichia coli. Being in the highest tertile in terms of growth of BV-associated microorganisms increased PID risk (adjusted rate ratio = 2.03, 95% confidence interval: 1.16, 3.53). Carriage of non-BV-associated microorganisms did not increase PID risk. Women with heavy growth of BV-associated microorganisms and a new sexual partner appeared to be at particularly high risk (adjusted rate ratio = 8.77, 95% confidence interval: 1.11, 69.2). When identified by microbial culture, a combination of BV-related microorganisms significantly elevated the risk of acquiring PID.

Adolescent↗
PubMed2005

Using cluster analysis in program evaluation.

The conventional way to measure program impacts is to compute the average treatment effect; that is, the difference between a treatment group that received some intervention and a control group that did not. Recently, scholars have recognized that looking only at the average treatment effect may obscure impacts that accrue to subgroups. In an effort to inform subgroup analysis research, this article explains the challenge of treatment group heterogeneity. It then proposes using cluster analysis to identify otherwise difficult-to-identify subgroups within evaluation data. The approach maintains the integrity of the experimental evaluation design, thereby producing unbiased estimates of program impacts by subgroup. This method is applied to data from the evaluation of New York State's Child Assistance Program, a reform that intended to increase work and earnings among welfare recipients. The article interprets the substantive findings and then addresses the advantages and disadvantages of the proposed method.

Adult↗
PubMed1993

Amino acid sequence similarities between low molecular weight endo-1,4-beta-xylanases and family H cellulases revealed by clustering analysis.

The amino acid sequences of seventeen family G xylanases and the two known family H cellulases have been compared by hydrophobic cluster analysis. A weak but significant similarity was demonstrated between these two families suggesting that these enzymes share the same molecular mechanism and catalytic residues and that they have related 3D folds. The major differences were found in the N-terminal regions.

Amino Acid Sequence↗
PubMed1992

Dietary patterns of elderly Boston-area residents defined by cluster analysis.

The dietary patterns of 680 noninstitutionalized, predominantly white, elderly volunteers from the Boston area (447 women and 233 men) were examined by cluster analysis of food contribution to energy intake. Data were derived from 3-day dietary records. The four major patterns identified corresponded to high consumption of (a) alcohol, (b) milk, cereals, and fruits, (c) bread and poultry, and (d) meat and potatoes. The resulting clusters of subjects differed significantly in gender, education, income, and frequency of smoking. Those with diets high in milk, cereals, and fruits had the highest intakes of micronutrients and the best hematologic profile. Those with high meat and potato intakes had the lowest intakes of micronutrients and lowest levels of plasma folate and vitamin B-6. High alcohol consumers had lowest blood levels of riboflavin and vitamin B-12 and highest levels of high-density-lipoprotein cholesterol. Those with high bread and poultry intakes had lowest reported energy intakes, but, paradoxically, they had the highest mean body mass index. Neither total serum cholesterol nor cholesterol intake varied significantly among groups. Our findings suggest that the nutritional status of the elderly may be improved by promoting food patterns rich in milk, fruit, and cereals and by counseling the elderly to limit consumption of alcohol and meats high in saturated fats.

Aged↗
PubMed1994

A cluster analysis of the neurons of the rat interpeduncular nucleus.

The morphometric characteristics of the neurons of the interpeduncular nucleus (IPN) in the rat were investigated by cluster analysis in order to identify neuronal groups which are morphometrically homogeneous, and to define their position and density in the IPN subnuclei. Two clusters of cells were detected. Cluster 1 neurons had a larger perikaryal size with a mean cross-sectional area of 170 microns2 and a high nuclear/cytoplasmic ratio. They were located mainly in the pars dorsalis (37%) and pars medialis (34%) rather than in the pars lateralis (29%). Cluster 1 neurons were also more frequent at the rostral (31%) and caudal (57%) poles than in the central part of the IPN. Cluster 2 cells showed a smaller mean perikaryal area (110 microns2), a small nucleus and abundant cytoplasm. They were equally distributed throughout the whole IPN. These findings suggest the existence of a magnocellular region at the rostral pole of the IPN which has not been described previously. The presence of IPN regions endowed with specific cytoarchitectural characteristics is discussed with respect to the complex neurochemical organisation of the nucleus.

Animals↗