History of Cluster Analysis

2014 ◽  
pp. 159-178
Keyword(s):  
Cluster Analysis ◽  
History Of

scholarly journals Revisiting comorbidities in gout: a cluster analysis

2013 ◽  
Vol 74 (1) ◽  
pp. 142-147 ◽  
Author(s):  
Pascal Richette ◽  
Pierre Clerson ◽  
Laure Périssin ◽  
René-Marc Flipo ◽  
Thomas Bardin
Keyword(s):  
Cluster Analysis ◽  
Renal Failure ◽  
Large Cohort ◽  
High Rate ◽  
Cross Sectional ◽  
High Prevalence ◽  
History Of ◽  
Obesity Hypertension ◽  
Almost All

ObjectivesThe reciprocal links between comorbidities and gout are complex. We used cluster analysis to attempt to identify different phenotypes on the basis of comorbidities in a large cohort of patients with gout.MethodsThis was a cross-sectional multicentre study of 2763 gout patients conducted from November 2010 to May 2011. Cluster analysis was conducted separately for variables and for observations in patients, measuring proximity between variables and identifying homogeneous subgroups of patients. Variables used in both analyses were hypertension, obesity, diabetes, dyslipidaemia, heart failure, coronary heart disease, renal failure, liver disorders and cancer.ResultsComorbidities were common in this large cohort of patients with gout. Abdominal obesity, hypertension, metabolic syndrome and dyslipidaemia increased with gout duration, even after adjustment for age and sex. Five clusters (C1–C5) were found. Cluster C1 (n=332, 12%) consisted of patients with isolated gout and few comorbidities. In C2 (n=483, 17%), all patients were obese, with a high prevalence of hypertension. C3 (n=664, 24%) had the greatest proportion of patients with type 2 diabetes (75%). In C4 (n=782, 28%), almost all patients presented with dyslipidaemia (98%). Finally, C5 (n=502, 18%) consisted of almost all patients with a history of cardiovascular disease and renal failure, with a high rate of patients receiving diuretics.ConclusionsCluster analysis of comorbidities in gout allowed us to identify five different clinical phenotypes, which may reflect different pathophysiological processes in gout.

Deal of the Day

2016 ◽  
Vol 58 (1) ◽  
pp. 95-118
Author(s):  
Edward Boon ◽  
Nir Ofek
Keyword(s):  
Cluster Analysis ◽  
Small Group ◽  
Information Overload ◽  
Price Sensitivity ◽  
Purchase Frequency ◽  
Large Group ◽  
The Future ◽  
History Of ◽  
Restaurant Meals ◽  
Deal Of The Day

Deal of the day is a form of e-commerce in which an intermediary allows merchants access to a subscriber list, to promote their offerings at a discount. This study performs a cluster analysis on the purchase history of a deal intermediary, to identify customer segments based on their purchase frequency, price sensitivity and the types of deal they buy. Five segments were identified, including a large group of customers who made one purchase and then stopped buying, a small group of extremely deal-prone subscribers, and a segment that limits their purchases to very few types of product (e.g. restaurant meals or spa treatments). The findings further show that targeting deals to specific customers may be desirable in the future to prevent information overload and ensure loyalty.

scholarly journals Risk factors of Parkinson disease

Neurology ◽  
2020 ◽  
Vol 95 (18) ◽  
pp. e2500-e2508 ◽  
Author(s):  
Daniele Belvisi ◽  
Roberta Pellicciari ◽  
Andrea Fabbrini ◽  
Matteo Costanzo ◽  
Sara Pietracupa ◽  
...  
Keyword(s):  
Risk Factors ◽  
Cluster Analysis ◽  
Family History ◽  
Protective Factors ◽  
Parkinson Disease ◽  
Coffee Consumption ◽  
Additive Interaction ◽  
Logistic Regression Models ◽  
Toxic Agents ◽  
History Of

ObjectiveTo perform a simultaneous evaluation of potential risk/protective factors of Parkinson disease (PD) to identify independent risk/protective factors, to assess interaction among factors, and to determine whether identified risk factors predict etiologic subtypes of PD.MethodsWe designed a large case-control study assessing 31 protective/risk factors of PD, including environmental and lifestyle factors, comorbid conditions, and drugs. The study enrolled 694 patients with PD and 640 healthy controls from 6 neurologic centers. Data were analyzed by logistic regression models, additive interaction models, and cluster analysis.ResultsThe simultaneous assessment of 31 putative risk/protective factors of PD showed that only coffee consumption (odds ratio [OR] 0.6; 95% confidence interval [CI] 0.4–0.9), smoking (OR 0.7, 95% CI 0.6–0.9), physical activity (OR 0.8, 95% CI 0.7–0.9), family history of PD (OR 3.2, 95% CI 2.2–4.8), dyspepsia (OR 1.8, 95% CI 1.3–2.4), and exposure to pesticides (OR 2.3, 95% CI1.3–4.2), oils (OR 5.6, 95% CI 2.3–13.7), metals (OR 2.8, 95% CI 1.5–5.4), and general anesthesia (OR 6.1, 95% CI 2.9–12.7) were independently associated with PD. There was no evidence of interaction among risk/protective factors, but cluster analysis identified 4 subtypes with different risk factor profiles. In group 1, all patients had a family history of PD, while dyspepsia or exposure to toxic agents was present in 30% of patients. In groups 2 and 3, a family history of PD was lacking, while exposure to toxic agents (group 2) and dyspepsia (group 3) played major roles. Group 4 consisted of patients with no risk factors.ConclusionsThis study demonstrated that 9 factors independently modify PD risk by coexisting in the same patient rather than interacting with others. Our study suggests the need for future preventive strategies aimed at reducing the coexistence of different risk factors within the same participant.

scholarly journals Classification of Wheezing Children in Rural Bangladesh by Intensity of Ascaris Infection, Total and Specific IgE Levels, History of Pneumonia, and Other Risk Factors

2019 ◽  
Vol 2019 ◽  
pp. 1-8 ◽  
Author(s):  
Haruko Takeuchi ◽  
Md Alfazal Khan ◽  
Khalequz Zaman ◽  
Sayaka Takanashi ◽  
S. M. Tafsir Hasan ◽  
...  
Keyword(s):  
Risk Factors ◽  
Cluster Analysis ◽  
Hierarchical Cluster Analysis ◽  
Immunoglobulin E ◽  
Infection Intensity ◽  
Hierarchical Cluster ◽  
Lymphoid Cells ◽  
Rural Bangladesh ◽  
History Of ◽  
Human Immunity

Ascaris lumbricoides is the most common soil-transmitted helminth and infects 447 million people in impoverished areas worldwide. It causes serious morbidity including wheezing and influences various aspects of human immunity, such as type 2 innate lymphoid cells, regulatory T cell function, and acquired immunity. Thus, it is crucial to elucidate its influence on human immunity. We aimed to classify wheezing children based on their Ascaris infection intensity and other risk factors using hierarchical cluster analysis to determine the mechanisms of and the degree to which Ascaris contributes to childhood wheezing in rural Bangladesh. We analyzed relevant data collected in 2001. The participants included 219 5-year-old wheezing children who were randomly selected from 1705 children living in the Matlab Health and Demographic Surveillance area of the International Centre for Diarrhoeal Disease Research, Bangladesh. Hierarchical cluster analysis was conducted using variables of history of pneumonia, total and specific immunoglobulin E levels, Ascaris infection intensity, and parental asthma. Three distinct wheezing groups were identified. Children in Cluster 1 (n=50) had the highest titers of the total, anti-Ascaris, anti-Dermatophagoides pteronyssinus, and anticockroach IgEs and experienced the fewest episodes of pneumonia. Cluster 2 (n=114), the largest group, experienced few episodes of pneumonia and had the lowest titers of the total, anti-Ascaris, anti-Dp, and anticockroach IgEs. Cluster 3 (n=32) consisted of participants with the most episodes of pneumonia and lower titers of the total and specific IgEs. The extremely high prevalence of Ascaris infection found in Clusters 1-3 was 78%, 77%, and 72%, respectively. Childhood wheezing in rural Bangladesh could be divided into three groups, with 26% of wheezing attributable to anti-Ascaris IgE and 16% to history of pneumonia during early childhood, and 58% might have been due to Ascaris infection without elevated anti-Ascaris IgE.

scholarly journals Morphological characteristics of local populations of European vendace Coregonus albula (L.) in some lakes of Latvia during 50 years

2009 ◽  
Vol 63 (6) ◽  
pp. 271-278 ◽  
Author(s):  
Jeļena Oreha ◽  
Nataļja Škute
Keyword(s):  
Cluster Analysis ◽  
Statistical Analysis ◽  
Morphological Characteristics ◽  
Specific Form ◽  
Living Conditions ◽  
Local Conditions ◽  
Coregonus Albula ◽  
Local Populations ◽  
History Of

Morphological characteristics of local populations of European vendace Coregonus albula (L.) in some lakes of Latvia during 50 years The vendace (Coregonus albula (L.)) is a widespread fish in the waters of the Holarctic and has been much studied in regard to history of spread and evolution. Statistical analysis of morphometric and meristic changes in Coregonus albula in some Latvian lakes for the last 50 years was conducted. Cluster analysis of meristic parameters showed that the vendace populations of lakes Stirnu - Alūksnes have become closer in meristic properties while the vendace populations of lakes Drīdzis - Sventes have become more dissimilar. The diversity and variation of morphological characteristics of vendace in the studied lakes are within the range of variations of the European vendace (Coregonus albula), although due to its flexibility it makes a lake-specific form depending on the local conditions of each reservoir. The discovered differences in the vendace populations from Lakes Nirzas, Rāznas, Stirnu, Alūksnes, Drīdzis, and Sventes seem to have resulted solely from/due to the adaptation of vendace to living conditions, which has changed during the previous 50 years.

scholarly journals Fractal modeling and cluster analysis of social processes in Russia, 17th – 20th centuries

2020 ◽  
Author(s):  
D Zhukov ◽  
◽  
V. Kanishchev ◽  
S. Lyamin
Keyword(s):  
Cluster Analysis ◽  
Effective Means ◽  
Social Processes ◽  
Historical Research ◽  
Fractal Modeling ◽  
Demographic Processes ◽  
History Of ◽  
And Cluster Analysis ◽  
Fractal Simulation ◽  
Research Tools

The article is devoted to the heuristic possibilities of fractal simulation and cluster analysis in historical research. The authors presented examples of the application of these research tools to the study of historical demographic processes (XIX–XX centuries), to the history of the frontier (XVII – XIX centuries). The authors concluded that the mentioned toolkit is an effective means of typing objects.

scholarly journals Evolution of Piston-Engine Fighter Aircrafts: Cluster Analysis to Study the History of Technology. Part 2

2021 ◽  
pp. 24-78
Author(s):  
Yury Viktorovich Kuzmin
Keyword(s):  
Cluster Analysis ◽  
Technological Progress ◽  
Rapid Development ◽  
Weight Ratio ◽  
Maximum Speed ◽  
Piston Engine ◽  
Wing Load ◽  
Time Period ◽  
Power Load ◽  
History Of

The article carries out cluster analysis of serial piston-engine fighters. 672 modifications built in 26 countries from 1913 to 1951 are taken into account. Maximum speed, wing load, power load and weight ratio are chosen as coordinates. Clustering reveals pioneering design: the earliest models in each cluster. One can also measure the dynamics of ideas lifetime that is the period when similar aircrafts (those within one cluster) were manufactured following the same methodology. The frequency new clusters emerged demonstrates the speed of technological progress. The author has found the turning points of development. These are 1930 and 1935. Until 1930 flight characteristics were increasing both due to an increase in thrust-to-weight ratio and an increase of wing load. Later the thrust-to-weight ratio remains practically unchanged. In 1935 the first aircraft appeared from three of the 19 clusters at once. Among them are the little-known Yugoslav fighter Ikarus IK-2 and the Soviet I-16 Type 5. The change of leading countries and the lifetime of ideas have been studied as well. The interval between the manufacture of the first and the last model, related to the cluster, corresponds to the time period when designers followed the same approach when designing an aircraft. Between wars, this interval exceeded 10 years whereas during rapid development periods it was 5-6 years shorter.

scholarly journals Basic vocabulary of the Karelian and Vepsian languages in the linguistic and geographical aspect

2021 ◽  
Vol 11 (1) ◽  
pp. 90-101
Author(s):  
I. P. Novak ◽  
Keyword(s):  
Cluster Analysis ◽  
Statistical Method ◽  
Field Research ◽  
Research Centre ◽  
North East ◽  
The North ◽  
History Of ◽  
Dialect Classification ◽  
Basic Vocabulary ◽  
The Baltic

Introduction: Karelian and Vepsian vocabulary has been collected and studied by linguists from Russia and Finland for two centuries. An invaluable source for research in the dialectology of the North-East group of the Baltic-Finnish languages is the «Comparative and Onomasiological Dictionary of the Karelian, Vepsian and Sami Languages» (2007). The dictionary was prepared by staff of the Institute of Linguistics, Literature and History of the Karelian Research Centre of the Russian Academy of Sciences using field research data from 1979–1981. The article reports the main results of applying the statistical method of cluster analysis to the dictionary entries. Objective: the analysis of the basic vocabulary of the dialects of the Karelian and Vepsian languages in the linguistic and geographical aspect using the statistical method of cluster analysis (dialectometry method). Research materials: pre-encoded for being uploaded to the clustering software database lexical data from the «Comparative and Onomasiological Dictionary of the Karelian, Vepsian and Sami Languages» (about 43 thousand units). Results and novelty of the research: the scientific novelty of the research is the application of the statistical method of cluster analysis to large volumes of pre-encoded lexical dialect material. The results of the calculation confirm the conclusions made by linguists earlier regarding the unity of the Vepsian and Karelian languages, as well as the presence of a clear border between them. The question of determination of the linguistic status of the Ludic dialects, which has been the subject of discussions among Russian and Finnish linguists for decades, is resolved in favor of the Karelian dialect on the basis of the material involved in the analysis. The boundaries between clusters outlined by the clustering program for the Vepsian language coincided with its dialect classification. On the Karelian part of the final map, the main bundle of isoglosses shifted north of the border between the dialects of the language, which indicates a more mobile character of its lexical level. The results presented in the article and the method of obtaining them will be later used to develop a linguistically grounded classification of Karelian language dialects.

scholarly journals Epidemiology of SARS-CoV-2 infection in Karnataka State, South India: Transmission dynamics of symptomatic vs. asymptomatic infections

2020 ◽  
Author(s):  
Narendra Kumar ◽  
Shafeeq K S Hameed ◽  
Giridhara R Babu ◽  
Manjunatha M Venkataswamy ◽  
Prameela Dinesh ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Respiratory Illness ◽  
The State ◽  
Contact Tracing ◽  
State Surveillance ◽  
History Of ◽  
Asymptomatic Infections ◽  
Clinical Illness ◽  
Spread Of Infection ◽  
Age Range

Background: In this report, we describe the epidemiology of SARS-CoV-2 infection, specifically examining how the symptomatic persons drove the transmission in the state of Karnataka, India, during the lockdown phase. Methods: The study included all the cases reported from March 8 to May 31, 2020 in the state. Any person with history of international or domestic travel from high burden states, those presenting with Influenza-like or Severe Acute Respiratory Illness and high-risk contacts of COVID19 cases, who were SARS-CoV-2 RT-PCR positive were included. Detailed analysis based on contact tracing data available from line-list of the state surveillance unit was performed using cluster analysis software package. Findings: Amongst the 3404 COVID-19 positive cases, 3096 (91%) were asymptomatic while 308 (9%) were symptomatic. Majority of the asymptomatic cases were in the age range of 16-50 years while symptomatic cases were between 31-65 years. Most of those affected were males. Cluster analysis of 822 cases indicated that the secondary attack rate, size of the cluster (dispersion) and occurrence of overt clinical illness is significantly higher when the index case in a cluster was symptomatic compared to an asymptomatic. Interpretation: Our findings indicate that both asymptomatic and symptomatic SARS-CoV-2 cases transmit the infection; however, the main driving force behind the spread of infection within the state was significantly higher from symptomatic cases. This has major implications for policies related to testing. Active search for symptomatic cases, subjecting them to testing and treatment should be prioritized for containing the spread of COVID-19.

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