The use of cluster analysis in the nursing literature is limited to the creation of classifications of homogeneous groups and the discovery of new relationships. As such, it is important to provide clarity regarding its use and potential. The purpose of this article is to provide an introduction to distance-based, partitioning-based, and model-based cluster analysis methods commonly utilized in the nursing literature, provide a brief historical overview on the use of cluster analysis in nursing literature, and provide suggestions for future research. An electronic search included three bibliographic databases, PubMed, CINAHL and Web of Science. Key terms were cluster analysis and nursing. The use of cluster analysis in the nursing literature is increasing and expanding. The increased use of cluster analysis in the nursing literature is positioning this statistical method to result in insights that have the potential to change clinical practice.
AbbasO. A. (2008). Comparisons between data clustering algorithms. International Arab Journal of Information Technology, 5, 320-325.
2.
AldenderferM. S.BlashfieldR. K. (Eds.). (1984). Cluster analysis. SAGE University paper series on quantitative applications in the social sciences, series no 07-044 (6th ed.). Newbury Park, CA: SAGE.
3.
AllredC. A.MichelY.ArfordP. H.CarterV.VeitchJ. S.DringR.. . .FinchN. J. (1994). Environmental uncertainty: Implications for practice model redesign. Nursing Economic$, 12, 318-326.
4.
BecksteadJ. (2002). Using hierarchical cluster analysis in nursing research. Western Journal of Nursing Research, 24, 307-319. doi:10.1177/01939450222045923
5.
BenderC. M.ErgunF. S.RosenzweigM. Q.CohenS. M.SereikaS. M. (2005). Symptom clusters in breast cancer across 3 phases of the disease. Cancer Nursing, 28, 219-225.
6.
BoudierA.CurjuricI.BasagañaX.HazguiH.AntoJ. M.BousquetJ.. . .SunyerJ. (2013). Ten-year follow-up of cluster-based asthma phenotypes in adults. A pooled analysis of three cohorts. American Journal of Respiratory & Critical Care Medicine, 188, 550-560. doi:10.1164/rccm.201301-0156OC
7.
BuijckB. I.ZuidemaS. U.EijkM. S.BorH.GerritsenD. L.KoopmansR. T. (2012). Is patient-grouping on basis of condition on admission indicative for discharge destination in geriatric stroke patients after rehabilitation in skilled nursing facilities? The results of a cluster analysis. BMC Health Services Research, 12, 443. doi:10.1186/1472-6963-12-443
8.
CollinsL. M.LansaS. T. (2010). Latent class and latent transition analysis: With applications in the social, behavioral, and health sciences. Hoboken, NJ: John Wiley.
9.
GhoshD.SternsA. A.DrewB. L.HameraE. (2011). Geospatial study of psychiatric mental health-advanced practice registered nurses (PMH-APRNs) in the United States. Psychiatric Services, 62, 1506-1509.
10.
Gilbertson-WhiteS.MiaskowskiC.LeeK.DoddM.WestC.CooperB. (2007). The stability of patient subgroups identified using cluster analysis. Oncology Nursing Forum, 34(1), 202-203.
11.
HaganD. E. (1977). Cluster analysis: An empirical methodology for developing homogeneous patient subjects for research purposes. Communicating Nursing Research, 9, 395-407.
12.
HaldarP.PavordI. D.ShawD. E.BerryM. A.ThomasM.BrightlingC. E.. . .GreenR. H. (2008). Cluster analysis and clinical asthma phenotypes. American Journal of Respiratory & Critical Care Medicine, 178, 218-224.
13.
HalkidiM.BatistakisY.VazirgiannisM. (2001). On clustering validation techniques. Journal of Intelligent Information Systems, 17, 107-145. doi:10.1023/A:1012801612483
14.
HalkidiM.BatistakisY.VazirgiannisM. (2002). Cluster validity methods: Part I. SIGMOD Record, 31(2), 40-45. doi:10.1145/565117.565124
15.
Hall-LordM. L.LarssonG.SteenB. (1999). Chronic pain and distress in older people: A cluster analysis. International Journal of Nursing Practice, 5, 78-85.
16.
HertzogM. A.PozehlB.DuncanK. (2010). Cluster analysis of symptom occurrence to identify subgroups of heart failure patients: A pilot study. The Journal of Cardiovascular Nursing, 25, 273-283. doi:10.1097/JCN.0b013e3181cfbb6c
17.
HillhouseJ. J.AdlerC. M. (1997). Investigating stress effect patterns in hospital staff nurses: Results of a cluster analysis. Social Science & Medicine, 45, 1781-1788. doi:10.1016/S0277-9536(97)00109-3
18.
HubaG. J.BriefD. E.CherinD. A.PanterA. T.MelchiorL. A. (1998). A typology of service patterns in end-stage AIDS care: Relationships to the transprofessional model. Home Health Care Services Quarterly, 17, 73-92.
19.
JainA. K. (2010). Data clustering: 50 years beyond K-means. Pattern Recognition Letters, 31, 651-666. doi:10.1016/j.patrec.2009.09.011
20.
JainA. K.MurtyM.FlynnP. (1999). Data clustering: A review. ACM Computing Surveys (CSUR), 31, 264-323. doi:10.1145/331499.331504
21.
JohnsonM. (1997). Using cluster analysis to develop a healing typology in vascular ulcers. Journal of Vascular Nursing, 15(2), 45-49. doi:10.1016/S1062-0303(97)90000-5
22.
KaufmanL.RousseauP. J. (2005). Finding groups in data: An introduction to cluster analysis. Hoboken, NJ: John Wiley.
23.
KrauseE. F. (1986). Taxicab geometry: An adventure in non-Euclidean geometry. New York, NY: Dover.
24.
KulczyckiA.QuH.BosargeP. M.ShewchukR. M. (2010). Examining the diverse perspectives of nurse practitioners regarding obstacles to diaphragm prescription: A latent class analysis. Journal of Women’s Health, 19, 1355-1361. doi:10.1089/jwh.2009.1730
25.
LeeK. S.SongE. K.LennieT. A.FrazierS. K.ChungM. L.HeoS.. . .MoserD. K. (2010). Symptom clusters in men and women with heart failure and their impact on cardiac event-free survival. The Journal of Cardiovascular Nursing, 25, 263-272. doi:10.1097/JCN.0b013e3181cfbb88
26.
LinC.ChengC.KuoS.ChouF. (2009). Development of a Chinese short form of the prenatal self-evaluation questionnaire. Journal of Clinical Nursing, 18, 659-666. doi:10.1111/j.1365-2702.2007.02201.x
27.
LindbergM.WikströmB.LindbergP. (2010). Subgroups of haemodialysis patients in relation to fluid intake restrictions: A cluster analytical approach. Journal of Clinical Nursing, 19, 2997-3005. doi:10.1111/j.1365-2702.2010.03372.x
28.
MagidsonJ.VermuntJ. K. (2002a). Latent class models for clustering: A comparison with K-means. Canadian Journal of Marketing Research, 20, 36-43.
29.
MagidsonJ.VermuntJ. K. (2002b). A nontechnical introduction to latent class models. Belmont, MA: Statistical Innovations Inc.
30.
MagidsonJ.VermuntJ. K. (2004). Latent class models: The SAGE handbook of quantitative methodology for the social sciences. Thousand Oaks, CA: SAGE.
31.
ManningC. D.RaghavanP.SchützeH. (2009). Hierarchical clustering. In ManningC. D.RaghavanP.SchützeH. (Eds.), An introduction to information retrieval (pp. 377-401). Cambridge, UK: Cambridge University Press.
32.
McSweeneyJ. C.ClevesM. A.ZhaoW.LeflerL. L.YangS. (2010). Cluster analysis of women’s prodromal and acute myocardial infarction symptoms by race and other characteristics. The Journal of Cardiovascular Nursing, 25, 311-322. doi:10.1097/JCN.0b013e3181cfba15
33.
MooreW. C.MeyersD. A.WenzelS. E.TeagueW. G.LiH.LiX.. . .BleeckerE. R. (2010). Identification of asthma phenotypes using cluster analysis in the severe asthma research program. American Journal of Respiratory and Critical Care Medicine, 181, 315-323. doi:10.1164/rccm.200906-0896OC
34.
NorušisM. J. (2008). Cluster analysis. In NorušisM. J. (Ed.), SPSS statistics 17.0: Advanced statistical procedures companion (pp. 361-391). Upper Saddle River, NJ: Prentice Hall.
35.
PopperK. R. (1968). Conjectures and refutations: The growth of scientific knowledge (5th ed.). New York, NY: Routledge Classics.
36.
RiegelB.HanlonA. L.McKinleyS.MoserD. K.MeischkeH.DoeringL. V.. . .DracupK. (2010). Differences in mortality in acute coronary syndrome symptom clusters. American Heart Journal, 159, 392-398. doi:10.1016/j.ahj.2010.01.003
37.
RokachL.MaimonO. (2005). Clustering methods. In MaimonO.RokachL. (Eds.), Data mining and knowledge discovery handbook (1st ed., pp. 321-352). New York, NY: Springer. doi:10.1007/b107408
38.
RosenfeldA. G.KnightE. P.SteffenA.BurkeL.DayaM.DeVonH. A. (2015). Symptom clusters in patients presenting to the emergency department with possible acute coronary syndrome differ by sex, age, and discharge diagnosis. Heart & Lung, 44, 368-375. doi:10.1016/j.hrtlng.2015.05.008
39.
RyanC. J.DevonH. A.HorneR.KingK. B.MilnerK.MoserD. K.. . .ZerwicJ. J. (2006). Symptom clusters in acute myocardial infarction. American Journal of Critical Care, 15, 337.
40.
ShahrbanianS.DuquetteP.KuspinarA.MayoN. E. (2015). Contribution of symptom clusters to multiple sclerosis consequences. Quality of Life Research, 24, 617-629. doi:10.1007/s11136-014-0804-7
41.
ShimanouchiS.KameiT.HayashiM. (2000). Home care for the frail elderly based on urinary incontinence level. Public Health Nursing, 17, 468-473.
42.
SirouxV.BasagaX.BoudierA.PinI.Garcia-AymerichJ.VesinA.. . .SunyerJ. (2011). Identifying adult asthma phenotypes using a clustering approach. European Respiratory Journal, 38, 310-317.
43.
SokalR. R.SneathP. H. (1963). Principles of numerical taxonomy. San Francisco, CA: W.H. Freeman.
44.
SørlieT.TibshiraniR.ParkerJ.HastieT.MarronJ. S.NobelA.. . .BotsteinD. (2003). Repeated observation of breast tumor subtypes in independent gene expression data sets. Proceedings of the National Academy of Sciences of the United States of America, 100, 8418-8423. doi:10.1073/pnas.0932692100
45.
SPSS Inc. (2001). The SPSS TwoStep cluster component. A scalable component to segment your customers more effectively (White paper, technical report). Chicago, IL: Author.
46.
StuifbergenA. K. (1990). Patterns of functioning in families with a chronically ill parent: An exploratory study. Research in Nursing & Health, 13, 35-44.
47.
TanP.SteinbachM.KumarV. (2005). Cluster analysis. In TanP.SteinbachM.KumarV. (Eds.), Introduction to data mining (1st ed., pp. 7-65). Boston, MA: Addison-Wesley Longman.
48.
ThorpeK.LooR. (2003). Critical-thinking types among nursing and management undergraduates. Nurse Education Today, 23, 566-574. doi:10.1016/S0260-6917(03)00102-3
49.
van ‘t VeerL. J.DaiH.vand. V.HeY. D.HartA. A. M.MaoM.. . .FriendS. H. (2002). Gene expression profiling predicts clinical outcome of breast cancer. Nature, 415, 530-536.
50.
VasiliadouA.KarvountzisG. G.SoumilasA.RoumeliotisD.TheodosopoulouE. (1995). Occupational low-back pain in nursing staff in a Greek Hospital. Journal of Advanced Nursing, 21, 125-130.
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
