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PubMed1991

[Classification of gamblers from self-help groups using cluster analysis].

In an empirically based classification by cluster analysis of 437 gamblers from self-help groups five distinct homogeneous subgroups were determined on the basis of such characteristics as frequency of gambling of various kinds, function of gambling and sensation during gambling, symptoms of pathological gambling as well as personality characteristics. These can be characterized as: Pathological slot-machine gamblers with 1) an emotionally instable, depressive-aggressive personality structure and 2) an emotionally instable, depressive personality structure; 3) pathological gamblers on German-style slot-machines and 4) pathological gamblers on classical games of chance--both without conspicuous personality, and 5) gamblers on German-style slot-machines under a subjective strain. On the whole, the distinctions are due to psychological variables, the social data hardly differ. A comparison of the subgroups on the basis of variables regarding the course and result of treatment shows that the pathological gamblers with a conspicuous personality structure more often failed to reach, the goal of abstinence set by "Gamblers Anonymous" and instead report about an improvement of their gambling behaviour. On the other hand, the gamblers on German-style slot-machines who were under a subjective strain more often found it easier to stop gambling completely. The results of the cluster analysis are compared with clinical diagnostic classifications of gamblers who received out-patient or in-patient treatment as well as with empirical classifications of addicts, and first hypotheses of a differential therapy indication are being discussed.

Adolescent↗
PubMed2003

Classification of Fourier transform infrared microscopic imaging data of human breast cells by cluster analysis and artificial neural networks.

Cluster analysis and artificial neural networks (ANNs) are applied to the automated assessment of disease state in Fourier transform infrared microscopic imaging measurements of normal and carcinomatous immortalized human breast cell lines. K-means clustering is used to implement an automated algorithm for the assignment of pixels in the image to cell and non-cell categories. Cell pixels are subsequently classified into carcinoma and normal categories through the use of a feed-forward ANN computed with the Broyden-Fletcher-Goldfarb-Shanno training algorithm. Inputs to the ANN consist of principal component scores computed from Fourier filtered absorbance data. A grid search optimization procedure is used to identify the optimal network architecture and filter frequency response. Data from three images corresponding to normal cells, carcinoma cells, and a mixture of normal and carcinoma cells are used to build and test the classification methodology. A successful classifier is developed through this work, although differences in the spectral backgrounds between the three images are observed to complicate the classification problem. The robustness of the final classifier is improved through the use of a rejection threshold procedure to prevent classification of outlying pixels.

Algorithms↗
PubMedDate not supplied

Cluster: a software system for epidemiologic cluster analysis.

A software package for cluster analysis was developed that incorporates 12 methods to analyse residence and time data, and morbidity or mortality rates. The Knox, Barton, Chen and Ohno methods of the software are illustrated with data for 123 cancer cases reported by a citizen requesting a cluster investigation. Longitude and latitude for each address were assigned by a geographic information system for analyses with the Barton (p > 0.05) and Knox methods (p > 0.05). The data were also analysed for time of the event with the Chen method, comparing the observed incidence to expected county disease rates (p = 0.61), and for temporal patterns within the geographically defined population with the Ohno method (p >0.05). It was concluded that no statistically significant disease cluster existed.

Adolescent↗
PubMed2003

Assessing antibiotic resistance in fecal Escherichia coli in young calves using cluster analysis techniques.

This study uses cluster analysis techniques to describe the antibiotic susceptibility patterns seen in calf fecal Escherichia coli (E. coli). Cohorts of 30 dairy calves at six farms were sampled at 2-week intervals during the pre-weaning period. At each sampling occasion five fecal E. coli isolates per calf were analyzed for antibiotic susceptibility to 12 antibiotics using the disk diffusion method. All isolates had a profile consisting of the aggregate measured inhibition zone size for each of the evaluated antibiotics. Several cluster analytic algorithms were assessed to partition the E. coli isolates. For our data, Ward's minimum variance method met the objectives of the study. Relative to the number of possible combinations of resistance clusters, a parsimonious set of 14 patterns was developed. This set of E. coli isolates exhibited a limited set of resistance patterns to the different antibiotics indicating that certain resistance genes may be linked.

Animals↗
PubMed2005

Cluster analysis of schistosome-specific antibody responses partitions the population into distinct epidemiological groups.

Immuno-epidemiological studies in schistosomiasis continue to generate large amounts of immunology data, whose analysis requires sophisticated statistical approaches. Here cluster analysis, is used to explore the relationship between immune responses and observed epidemiological patterns of schistosome infection in two Zimbabwean communities. Analysis of cross-sectional antibody data (IgA, IgE, IgG1, IgG2, IgG3, IgG4 and IgM directed against Schistosoma haematobium soluble egg antigen (SEA)) showed that cluster analysis partitioned the data into distinct epidemiological groups based on all seven antibody isotypes (defined by age, infection intensity, treatment status and history of infection) confirming an already known partitioning based on IgA/IgG1 production. All treated participants (children) changed cluster membership following treatment from clusters where IgA was the predominant antibody to clusters where IgG1 predominated. There was a differential distribution of IgE and IgG4 between clusters consistent with the recently proposed balance between T-helper cells (Th) 1, Th2 and regulatory T cells. The analysis suggested that naturally acquired anti-schistosome responses associated with resistance to infection were different from drug-induced responses associated with resistance to re-infection. Furthermore, the analysis suggested that parasite-specific immune responses were dynamic. The analysis conducted on data from participants resident in the S. mansoni endemic area who were all children partitioned the data into two clusters, one with predominately pre-treatment data (cluster 1) and the other with post-treatment data (cluster 2). The antibody profiles of both clusters were most similar to the profile of people with a modified Th2 response. Following treatment 43% of the children in cluster 1 moved to cluster 2, which generally had higher levels of antibodies. A detailed study of factors determining which children moved between the clusters showed that it was mostly the older, infected children who moved to cluster 2. The results of the analysis are discussed in terms of current theories of the development of acquired immunity to schistosomiasis. The relative merits of cluster analysis as a statistical tool for analysing these data are also discussed.

Age Factors↗
PubMedDate not supplied

A cluster analysis of manic states.

A cluster analysis was performed on 81 manically disturbed patients assessed at interview on items of manic symptomatology and general psychopathology. Four groups were obtained: (1) a mildly excited group, (2) a group characterized by elation and speech disturbance, (3) a small severely disturbed excited group, more schizophrenic than manic, and (4) a group characterized by aggressive overactivity.

Aggression↗
PubMed2001

Cluster analysis on multiple drugs susceptibility supplements genotyping of methicillin-resistant Staphylococcus aureus.

OBJECTIVE: To evaluate the typing power of cluster analysis of antimicrobial susceptibility. METHODS: Results of pulsed-field gel electrophoresis in 71 strains of methicillin-resistant Staphylococcus aureus were compared with cluster analysis of the diameter of growth inhibition in 11 drugs. Subjects were a consecutive series of patients (n = 71) from the wards and outpatient units of a community teaching hospital. RESULTS: The cluster analysis took 2 to 3 seconds once the data were entered into a computer. The sensitivity, specificity, and accuracy of the cluster analysis were 76.3%, 58.3%, and 73.2%, respectively, using genotyping as the reference. CONCLUSIONS: The cluster analysis offered real-time epidemiologic data at minimal cost and labor, warranting its cost-effective role.

Bacterial Typing Techniques↗
PubMed1993

MANSEK and SUNHCA. Two interactive programs for the hydrophobic cluster analysis of protein sequences.

Hydrophobic cluster analysis (HCA) is an efficient method for analysing and comparing the amino acid sequences of proteins. It relies on two-dimensional representations of the sequences presently generated by simple plot programs working on microcomputers. Two interactive programs, MANSEK and SUNHCA, are described here that operate from Vax and Sun workstations respectively. These programs allow the display of several protein sequences in the form of two-dimensional helical plots suitable for HCA. Several tedious, repetitive and time-consuming steps of HCA have been suppressed by implementing several features such as interactive on-screen manipulations (zoom, translations) of the plots and HCA score calculations on segments chosen by the user. Plots on paper can be obtained through hard copies or plotting subroutines.

Algorithms↗
PubMed1985

Cluster analysis in predicting the carcinogenicity of chemicals using short-term assays.

Cluster analysis can be a useful tool for exploratory data analysis to uncover natural groupings in data, and initiate new ideas and hypotheses about such groupings. When applied to short-term assay results, it provides and improves estimates for the sensitivity and specificity of assays, provides indications of association between assays and, in turn, which assays can be substituted for one another in a battery, and allows a data base containing test results on chemicals of unknown carcinogenicity to be linked to a data base for which animal carcinogenicity data are available. Cluster analysis was applied to the Gene-Tox data base (which contains short-term test results on chemicals of both known and unknown carcinogenicity). The results on chemicals of known carcinogenicity were different from those obtained when the entire data base was analyzed. This suggests that the associations (and possibly the sensitivities and specificities) which are based on chemicals of known carcinogenicity may not be representative of the true measures. Cluster analysis applied to the total data base should be useful in improving these estimates. Many of the associations between the assays which were found through the use of cluster analysis could be 'validated' based on previous knowledge of the mechanistic basis of the various tests, but some of the associations were unsuspected. These associations may be a reflection of a non-ideal data base. As additional data becomes available and new clustering techniques for handling non-ideal data bases are developed, results from such analyses could play an increasing role in strengthening prediction schemes which utilize short-term tests results to screen chemicals for carcinogenicity, such as the carcinogenicity and battery selection (CPBS) method (Chankong et al., 1985).

Carcinogens↗
PubMed2000

Computer recognition of skin structures using discriminant and cluster analysis.

BACKGROUND/AIMS: Automated image analysis of complex tissues is usually limited by the difficulty of recognizing special structures by computer. The aim of this study was to test the applicability of discriminant and cluster analysis to the interpretation of skin images. METHODS: Digital images from microscopic, dermatoscopic and clinical views of skin specimens were electronically dissected into elements of equal size and shape, and a set of grey level, colour and texture features was assessed for each element. Elements were classified interactively and submitted to discriminant analysis. Furthermore, hierarchical cluster analysis was used to enable the system to classify the tissue elements automatically, based on the available digital information. The classification results were relocated to the original image in order to evaluate the performance of the procedure. RESULTS: The system performs well in reproducibly detecting different skin structures in digital images. Discriminant analysis of interactively classified elements yielded a correct reclassification in 98 to 100% of tissue elements. Among the cluster analysis procedures, the conservative Ward method after removal of all highly correlated features produced the best results. The method turned out to be applicable irrespective of the image source used. CONCLUSIONS: Discriminant and cluster analysis may be helpful techniques for a user-independent, subjectively unbiased measurement system of skin structures.

Journal Article↗
PubMed1989

Hydrophobic cluster analysis (HCA) of the hormone-binding domain of receptor proteins.

A new technique of protein sequence analysis, namely, Hydrophobic Cluster Analysis (HCA), has been used to align and compare the sequences of proteins belonging to the receptor superfamily (steroid, thyroid hormone and retinoic acid receptors) and serpin superfamily (corticosteroid binding globulin (CBG) and alpha 1-antitrypsin (alpha 1-AT]. By matching up clusters of hydrophobic amino-acids that oftenmost correspond to identifiable secondary structures (alpha-helices, beta-strands etc.), it has been possible to deduce the following information on the secondary structures of these proteins: CBG is structurally related to alpha 1-AT (HCA score greater than 80%), the structures of the hormone-binding domains of the steroid receptors that bind 3-keto-delta 4-steroids are closely interrelated (greater than 80%) but less closely related to that of the estrogen receptor (ER) (approximately 75%), vitamin D, retinoic acid and thyroid hormone receptors are structurally closely related (greater than or equal to 80%). Their secondary structures are, however, also related to that of the steroid receptors (approximately 70%), and a high degree of analogy exists between the structures of serpins and of the hormone-binding domains of members of the steroid superfamily (60-70%). HCA has clearly shown that a previous local sequence alignment of the estrogen receptor with other steroid receptors and cytochromes P450 has to be reconsidered. The published consensus steroid binding sequence previously identified in cytochromes is in fact 80 amino-acids upstream from its previously defined position. Other regions of contiguous sequence identity have also been identified which may be involved in the hydrophobic core of the protein or in steroid binding. Their positions have been indicated using the crystal structure of alpha 1-AT as a model.

Amino Acid Sequence↗
PubMed1978

A note on cluster analysis and depression: disparities in results produced by the application of different clustering methods.

Cluster analysis is the most logically suited method for establishing psychiatric classifications. Different mathematical methods of clustering do, however, produce disparate results when applied to the same set of data. This study attempted to quantify the extent of such disparities, and found them to be marked. It was concluded that until cluster analysis has undergone further mathematical and statistical development, it should be used with caution.

Adult↗
PubMed1999

Use of cluster analysis to validate IHS diagnostic criteria for migraine and tension-type headache.

Cluster analysis was used to validate headache diagnostic criteria of the International Headache Society (IHS). Structured diagnostic interviews were conducted on 443 headache sufferers from a community sample, which was randomly split to allow replication. Hierarchical cluster analysis of symptoms in both subsamples revealed two distinct (P<.001) clusters: (1) unilateral pulsating pain, pain aggravated by activity, and photophobia and phonophobia, and (2) bilateral pressing/tightening pain, mild to moderate intensity, and absence of nausea/vomiting. These clusters were consistent with IHS migraine and tension-type classifications, respectively. Replication using a non-hierarchical clustering technique, k-means cluster analysis, revealed a migrainelike patient cluster, reflecting more frequent pulsating, unilateral pain; more severe pain; and pain aggravated by activity; nausea, vomiting, photophobia, and phonophobia. A tensionlike patient cluster was also identified, reflecting more frequent pressing/tightening pain, mild to moderate pain, bilateral location, and absence of nausea/vomiting. These patient clusters were consistent across subsamples. International Headache Society diagnoses corresponded with classification based upon statistically derived clusters (P<.001). These results indicate that headache symptoms cluster empirically in a manner consistent with IHS criteria for migraine and tension-type headaches. Criterion overlap problems regarding pain intensity and duration were identified. Overall, these data support migraine and tension-type headache as distinct entities, and provide support for the IHS diagnostic criteria with minor modifications.

Adult↗
PubMed2002

Abdominal and erector spinae muscle activity during gait: the use of cluster analysis to identify patterns of activity.

OBJECTIVE: To describe patterns of muscle activation during gait in selected abdominal and lumbar muscles using cluster analysis. PARTICIPANTS: A sample of convenience of 38 healthy adult volunteers. Outcome measures. Electromyographic activity from the right internal and external obliques, rectus abdominis and lumbar erector spinae were recorded, and the root mean square values for each muscle were calculated throughout the stride in 5% epochs. These values were normalised to maximum effort isometric muscle contractions. Cluster analysis was used to identify groups of subjects with similar patterns of activity and activation levels. RESULTS: Cluster analysis identified two patterns of activity for the internal oblique, external oblique and rectus abdominis muscles. In the lumbar erector spinae, three patterns of activity were observed. In most instances, the patterns observed for each muscle differed in the magnitude of the activation levels. In rectus abdominis and external oblique muscles, the majority of subjects had low levels of activity (<5.0% of a maximum voluntary contraction) that were relatively constant throughout the stride cycle. In the internal oblique and the erector spinae muscles, more distinct bursts of activity were observed, most often close to foot-strike. The different algorithms used for the cluster analysis yielded similar results and a discriminant function analysis provided further evidence to support the patterns observed. CONCLUSIONS: Cluster analysis was useful in grouping subjects who had similar patterns of muscle activity. It provided evidence that there were subgroups that might otherwise not be observed if a group ensemble was presented as the "norm" for any particular muscle's role during gait. RELEVANCE: The identification of common variations in muscle activity may prove valuable in identifying individuals with electromyographic patterns that might influence their chances of sustaining injury. Alternatively, clusters may provide important information related to muscle activity in those that do well or otherwise after a particular injury.

Abdominal Muscles↗
PubMed1997

An automated procedure for cluster analysis of multivariate satellite data.

This paper studies the applicability of available cluster analysis methods on multivariate satellite data. Four different methods are studied, these are the Principal Component Analysis (PCA), K-means, Self Organizing Maps (SOM) and the Adaptive Resonance Theory (ART). Special focus is placed on the usefulness of these cluster analysis methods, that is, if the results generated by these methods are relevant and are of help to the physical analysis of the data. A combined SOM adaptive dynamic K-means procedure suitable for automated cluster analysis is presented. This new method is capable of achieving useful partition of multivariate satellite data which has not previously been studied.

Algorithms↗
PubMed2003

Detection of single unit activity from the rat vagus using cluster analysis of principal components.

In vivo recordings from subdiaphragmatic vagal afferent nerves generally lack the resolution to distinguish single unit activity. Several methods for data acquisition and analysis were combined to produce a high degree of reliability in recording electrophysiological signals from gastrointestinal and hepatic afferent fibers in the rat. Recordings with low noise were achieved by paralysis of the respiratory muscles and by pinning the nerve to a recording platform. Single unit activity was isolated using principal component (PC) analysis and cluster cutting of data in multi-dimensional space (1-3 PCs). Cluster assignments were determined by a semi-automated approach using the k-means algorithm. The accuracy of single unit classification was assessed by checking inter-spike intervals (ISIs) to determine the length of the refractory period, and by cross-correlation analysis to assess whether single units were mistakenly split into more than one cluster. These analyses produced up to four isolated single units from each nerve filament (a bundle of nerve fibers), and typically it was possible to further increase yield by recording from several nerve filaments simultaneously using an array of electrodes.

Action Potentials↗
PubMed1999

Hierarchical cluster analysis applied to workers' exposures in fiberglass insulation manufacturing.

The objectives of this study were to explore the application of cluster analysis to the characterization of multiple exposures in industrial hygiene practice and to compare exposure groupings based on the result from cluster analysis with that based on non-measurement-based approaches commonly used in epidemiology. Cluster analysis was performed for 37 workers simultaneously exposed to three agents (endotoxin, phenolic compounds and formaldehyde) in fiberglass insulation manufacturing. Different clustering algorithms, including complete-linkage (or farthest-neighbor), single-linkage (or nearest-neighbor), group-average and model-based clustering approaches, were used to construct the tree structures from which clusters can be formed. Differences were observed between the exposure clusters constructed by these different clustering algorithms. When contrasting the exposure classification based on tree structures with that based on non-measurement-based information, the results indicate that the exposure clusters identified from the tree structures had little in common with the classification results from either the traditional exposure zone or the work group classification approach. In terms of the defining homogeneous exposure groups or from the standpoint of health risk, some toxicological normalization in the components of the exposure vector appears to be required in order to form meaningful exposure groupings from cluster analysis. Finally, it remains important to see if the lack of correspondence between exposure groups based on epidemiological classification and measurement data is a peculiarity of the data or a more general problem in multivariate exposure analysis.

Air Pollutants, Occupational↗