Implementation of Chaid Algorithm
Celal Hakan Kagnicioglu · Mune Mogol
International Journal of Research in Business and Social Science · 2014 · Vol. 3 / Issue 4 · 42-51
Publication metrics
External links open provider records or DOI searches. Provider counts are not combined. Mendeley readership measures saves, not citations.
Dimensions
Live provider badge. If unavailable, use the Dimensions link above.
Observed 21 Oct 2026, 07:22 UTC
Observed 21 Oct 2026 00:49 UTC
OpenAlex and Mendeley observations refresh on article visits, at most once per 24 hours. Unavailable data is not zero. Google Scholar opens a DOI search.
Citations received by this article, grouped by the year of the citing work. Crossref and OpenAlex lifetime totals remain separate in Publication metrics.
2017–2026 Annual history last updated:
Citations received
OpenAlex · year of the citing work
Hover over a year or focus the chart and use arrow keys to inspect values.
2017–2026 annual-history window. 2026 is year to date. Unknown years have gaps.
View data & coverage
2026 is incomplete. Within a successfully fetched ten-year window, omitted years mean zero. Years beyond a saved observation’s window remain unknown (—). Older citations are excluded, so this chart’s sum may differ from the OpenAlex lifetime total. The date above belongs to this annual history; an unsuccessful refresh keeps the previous observation.
Abstract

 
 
 Today, companies are planning their own activities depending on efficiency and effectiveness. In order to have plans for the future activities they need historical data coming from outside and inside of the companies. However, this data is in huge amounts to understand easily. Since, this huge amount of data creates complexity in business for many industries like hospitality industry, reliable, accurate and fast access to this data is to be one of the greatest problems. Besides, management of this data is another big problem. In order to analyze this huge amount of data, Data Mining (DM) tools, can be used effectively. In this study, after giving brief definition about fundamentals of data mining, Chi Squared Automatic Interaction Detection (CHAID) algorithm, one of the mostly used DM tool, will be introduced. By CHAID algorithm, the most used materials in room cleaning process and the relations of these materials based on in a five star hotel data are tried to be determined. At the end of the analysis, it is seen that while some variables have strong relation with the number of rooms cleaned in the hotel, the others have no or weak relation.
 
 
Keywords: {'name': 'Data Mining'}, {'name': 'CHAID'}, {'name': 'Tourism'}, {'name': 'Hotel'}
Authors & affiliations
Celal Hakan Kagnicioglu
Affiliation not supplied
Mune Mogol
Affiliation not supplied
Deposited references
Supplied references, not a list of publications citing this article.
DOI: 10.1057/jt.2010.3
Becker, J. (2001). Implementing 5S to promote safety and housekeeping. Professional Safety, 46(8), 29 – 31.
DOI: 10.1017/S1833367200003837
Management &Organization, 13, 160 - 174.
Burt, V. (2010). Pros, cons of outsourcing housekeeping. H&MM , (4), 13 - 14.
Chen, K., C. (2004). Decision support systems for tourism development: system dynamic approach. Journal of
DOI: 10.1016/j.eswa.2008.04.003
DOI: 10.1016/j.eswa.2006.04.014
DOI: 10.1016/S0920-5489(01)00072-1
- 193.
DiGiulio, L. (2010). The welcomes IEHA. Executive Housekeeping Today, 14-16.
DOI: 10.1214/aos/1176344552
, (1), 1 - 26.
DOI: 10.1126/science.253.5018.390
DOI: 10.1016/S0278-4319(96)00053-9
International Journal of Hospitality Management, 16, (1), 99-117.
DOI: 10.1016/S0378-7206(99)00051-8
& Management, 37, 271 - 281.
Fox, J. Bootstrapping Regression Methods. http://cran.r-project.org/doc/contrib/Fox-Companion/appendix-
bootstrapping.pdf, 18.08.2014
DOI: 10.1108/10650759910276381
, (2), 81 - 90.
DOI: 10.1080/15256480802427313
International Journal of Hospitality&Tourism Administration, 9, (4), 365 - 383.
Grady, P. (2005). How preventative cleaning measures outside of our cleanroom will boost sanitation levels on the
inside. Total Facility Cleanliness, 33 - 35.
DOI: 10.1016/S0957-4174(98)00008-6
marketing. Expert Systems with Application, 15, (1), 1 - 31.
Han, J., Kamber, M., & Pei, J. (2012). Data mining: concepts and techniques. Burlington, MA: Elsevier, Hsu, C.H.C., & Kang, S.K. (2007). CHAID based segmentation international visitors’ trip characteristics and
perceptions. Journal of Travel Research 46: 207-216.
Hsu, S.-Y., Ho, T.-K., Tsai, J.-J., & Wang, C.-H. (2011). The evaluation mode of hotel housekeeping management.
African Journal of Business Management, 5, (34), 13249 - 13253.
DOI: 10.1016/j.ijinfomgt.2013.05.007
DOI: 10.1007/BF01589752
Jannadi, M., & Al-Isa, H. (1995). Effective industrial housekeeping: The supervisor's role. Professional Safety 40, (2),
-33.
DOI: 10.1057/thr.2009.11
DOI: 10.1002/hed.23058
Kappa, M.M., Nitchke, A., & Schappert P.B. (1995). Managing housekeeping operations. East Lansing, Mich.: Educational Institute American Hotel & Motel Association.
Karathiya, M.B., Sakshi, R.S., Sakshi, D., S., & Kathiriya, D.R. (2012). Data mining for travel and tourism. Journal of Information and Operations Management, 3, (1), 114 - 118.
DOI: 10.2307/2986296
DOI: 10.1016/j.atmosenv.2008.02.058
DOI: 10.1016/j.ijhm.2010.10.010
DOI: 10.1300/J171v02n01_02
DOI: 10.1007/978-0-387-09823-4
logistic regression. Journal of Business Research, 60, 656 - 662.
Medlik, S., & Ingram, H. (2000). The business of hotels. Oxford: Butterworth Heinemann.
Murphy, E.L., & Comiskey, C.M. (2013). Using Chi- Squared automatic interaction detection (CHAID) modelling to
identify group of methadone treatment clients experiencing significantly poorer treatment outcomes. Journal
of Substance Abuse Treatment, 45, 343 - 349.
O’bRien, J.A., & Marakad, G.M. (2011). Management information systems. Boston: McGraw Hill Irwin. Öztekin, A. (2011). A decision support system for usability evaluation of web based information systems. Expert
Systems and Application, 38, 2110-2118.
Peterson, R. (2011). Surviving the holidays. Executive Housekeeping Today, 6-8.
DOI: 10.1016/j.ijhm.2005.04.003
-312.
Rupnik, R., Kukar, M., Bajec, & M., Krisper, M. (2006). DMDSS: data mining based decision support systems to
integrate data mining and decision support. 28th Int. Conf. Information Technology Interface ITI, Cavtat,
Croatia. Management Information Technology. Upper Saddle River, N.J.: Pearson Prentice Hall. Rygielski, C., Wang, J.C., & Yen, D.C. (2002). Data mining techniques for customer relationship management.
Technology in Society. 24: 483-502.
DOI: 10.1016/0261-5177(92)90058-F