Identifying electronic gaming machine gambling personae through unsupervised session classification

Maria Gabriella Mosquera; Vlado Keselj

doi:10.3934/bdia.2017015

Article Contents

2017, Volume 2, Issue 2: 141-175. Doi: 10.3934/bdia.2017015

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Identifying electronic gaming machine gambling personae through unsupervised session classification

Faculty of Computer Science, Dalhousie University, 6050 University Avenue, Halifax, NS B3H 4R2, Canada

Received: April 30, 2017

Revised: September 30, 2017

Published: March 2017

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Abstract

The rising accessibility in gambling products, such as Electronic Gaming Machines (EGM), has increased interest in the effects of gambling; in particular, the potential for impulse control disorders, such as problem gambling. Nevertheless, empirical research of EGM gambling behaviour is scarce. In this exploratory study, we apply data mining techniques on 46,416 gambling sessions, collected in situ from 288 EGMs. Our research focused on identifying the at-risk behavioural markers of sessions to help distinguish gambling personae. Our data included measures of gambling involvement, out-of pocket expense of sessions, amount won, and cost of gambling. This research, discusses the methodology used to collect and analyze the required gambling measures, explains the criteria used for identifying valid sessions, and combines outlier mining methods to identify instances of heavily involved gambling (i.e., outliers). Our results suggest that sessions were classified as potential non-problem, potential low-risk, potential moderate risk, and potential problem gambling sessions. Further, outlier sessions were more heavily involved in terms of gambling intensity and amount redeemed, despite having low duration times. Finally, our methods suggest that the lack of player identification does not prevent one from identifying the potential incidence of problem gambling behaviour.

Keywords:

Mathematics Subject Classification: Primary: 91C20, 62H30; Secondary: 03C45.

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Figure 1. Binned EGM Gambling Sessions Based on Hours Played

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Figure 2. Valid EGM Gambling Sessions

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Figure 3. Test of Normality: Boxplot Analysis

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Figure 4. Test of Normality: Normal Q-Q Plot Analysis

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Figure 5. Scatterplot: Clustered sessions

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Figure 6. Between Groups One-Way ANOVA: Means Plots

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Figure 7. Boxplot Analysis on Clustered Data: Duration

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Figure 8. Scatterdot: Cluster 1 Outlier Analysis

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Figure 9. Scatterdot: Cluster 2 Outlier Analysis

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Figure 10. Scatterdot: Cluster 4 Outlier Analysis

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Table 1. Descriptive Statistics Measures of EGM Sessions

Variables	Mean	SD	Median	Mode	Max.	Min.
Duration^a	42.03	109.09	15.67	3.87	1099.72	0.17
Bets	373	606	170	3	13,282	2
Intensity	16	10	19	20	68	0
Redeemed^b	70.49	150.75	30.00	20.00	6,425.00	5.00
Vouchers^b	50.44	384.18	0.05	0	40,833.05	0
Net Loss^b	52.09	133.39	19.90	0	6,424.69	0
%Loss	75.46	40.97	99.97	100.00	100.00	0
a. Measured in minutes. b. Measured in Euros.

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Table 2. Spearman™s Rank-Order Correlation Analysis

Spearman™s Rho
		Duration	Bets	Intensity	Redeemed	Vouchers	Net Loss	%Loss
Duration	$r_{s}$	.	.515^**	-.208^**	.341^**	-.334^**	.374^**	.406^**
Duration	Sig.	.	.000	.000	.000	.000	.000	.000
Bets	$r_{s}$	.515^**	.	.606^**	.690^**	.224^**	.356^**	-.150^**
Bets	Sig.	.000	.	.000	.000	.000	.000	.000
Intensity	$r_{s}$	-.208^**	.606^**	.	.433^**	.640^**	-.003	-.608^**
Intensity	Sig.	.000	.000	.000	.000	.511	.000	0.000
Redeemed	$r_{s}$	.341^**	.690^**	.433^**	.	.177^**	.584^**	-.068^**
Redeemed	Sig.	.000	.000	.000	.	.000	.000	.000
Vouchers	$r_{s}$	-.334^**	.224^**	.640^**	.177^**	.	-.512^**	-.980^**
Vouchers	Sig.	.000	.000	.000	.000	.	.000	.000
Net Loss	$r_{s}$	.374^**	.356^**	-.003	.584^**	-.512^**	.	.592^**
Net Loss	Sig.	.000	.000	.511	.000	.000	.	.000
%Loss	$r_{s}$	.406^**	-.150^**	-.608^**	-.068^**	-.980^**	.592^**	.
%Loss	Sig.	.000	.000	.000	.000	.000	.000	.
** Correlation is significant at the 0.01 level (2-tailed).

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Table 3. Crosstabulation: Sample 1 v. Full Sample (k = 4)

Cluster Membership (Sample 1) v. Cluster Membership (Full Sample) Crosstabulation)
			Cluster Mship. (Full Sample)
			1	2	3	4	Total
Cluster Mship.	1	Count	6944	15	0	0	6,959
(Sample 1)		Expected Count	2,071.5	2,171.1	123.2	2,593.2	6,959
		% within Cluster (sample 1)	99.8%	0.2%	0%	0%	100%
		% within Cluster (full sample)	100%	0.2%	0%	0%	29.8%
		% of Total	29.8%	0.1%	0%	0%	29.8%
	2	Count	0	7,263	0	44	7,307
		Expected Count	2,175.1	2,279.7	129.4	2,722.9	7,307
		% within Cluster (sample 1)	0%	99.4%	0%	0.6%	100%
		% within Cluster (full sample)	0%	99.8%	0%	0.5%	31.3%
		% of Total	0%	31.1%	0%	0.2%	31.3%
	3	Count	0	0	413	0	413
		Expected Count	122.9	128.9	7.3	153.9	413
		% within Cluster (sample 1)	0%	0%	100%	0%	100%
		% within Cluster (full sample)	0%	0%	100%	0%	1.8%
		% of Total	0%	0%	1.8%	0%	1.8%
	4	Count	0	0	0	8,649	8,649
		Expected Count	2,574.5	2,698.4	153.1	3,223	8,649
		% within Cluster (sample 1)	0%	0%	0%	100%	100%
		% within Cluster (full sample)	0%	0%	0%	99.5%	37.1%
		% of Total	0%	0%	0%	37.1%	37.1%
Total		Count	6,944	7,278	413	8,693	23,328
		Expected Count	6,944	7,278	413	8,693	23,328
		% within Cluster (sample 1)	29.80%	31.20%	1.8%	37.3%	100%
		% within Cluster (full sample)	100%	100%	100%	100%	100%
		% of Total	29.80%	31.20%	1.8%	37.3%	100%

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Table 4. Crosstabulation: Sample 2 v. Full Sample (k = 4)

Cluster Membership (Sample 2) v. Cluster Membership (Full Sample) Crosstabulation)
			Cluster Mship. (Full Sample)
			1	2	3	4	Total
Cluster Mship.	1	Count	6,871	0	0	0	6,871
(Sample 2)		Expected Count	2,053.1	2,148.1	122.3	2,547.5	6,871
		% within Cluster (sample 2)	100%	0%	0%	0%	100%
		% within Cluster (full sample)	99.6%	0%	0%	0%	29.8%
		% of Total	29.8%	0%	0%	0%	29.8%
	2	Count	28	7,162	0	0	7,190
		Expected Count	2,148.5	2247.8	128	2,665.7	7,190
		% within Cluster (sample 2)	0.4%	9, 9.6%	0%	0%	100%
		% within Cluster (full sample)	0.4%	99.2%	0%	0%	31.10%
		% of Total	0.1%	31.0%	0%	0%	31.10%
	3	Count	0	0	411	0	411
		Expected Count	122.8	128.5	7.3	152.4	411
		% within Cluster (sample 2)	0%	0%	100%	0%	100%
		% within Cluster (full sample)	0%	0%	100%	0%	1.8%
		% of Total	0%	0%	1.8%	0%	1.8%
	4	Count	0	56	0	8,560	8,616
		Expected Count	2,574.6	2,693.6	153.4	3,194.4	8,616
		% within Cluster (sample 2)	0%	0.6%	0%	99.4%	100%
		% within Cluster (full sample)	0%	0.8%	0%	100%	37.30%
		% of Total	0%	0.2%	0%	37.1%	37.3%
Total		Count	6,899	7,218	411	8,560	23,088
		Expected Count	6,899	7,218	411	8,560	23,088
		% within Cluster (sample 2)	29.9%	31.3%	1.8%	37.1%	100%
		% within Cluster (full sample)	100%	100%	100%	100%	100%
		% of Total	29.9%	31.3%	1.8%	37.1%	100%

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Table 5. Descriptive Statistics: Cluster 1 Sessions

Cluster 1 Sessions ^a
Variables	Mean	SD	Median	Max.	25th	75th
Duration$^{b}$	47.14	56.17	27.65	412.77	12.33	59.20
Bets	122	168	68	3,648	34	142
Intensity	4.09	3.10	3.35	11.27	1.32	6.58
Redeemed$^{c}$	30.02	45.04	20.00	1,215.00	10.00	40.00
Vouchers$^{c}$	1.60	23.41	.00	1,962.93	.00	.00
Net Loss$^{c}$	29.48	45.13	20.00	1,215.00	10.00	40.00
%Loss	97.36	15.70	100.00	100.00	100.00	100.00
a. n = 13,843 b. Measured in minutes. c. Measured in Euros.

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Table 6. Descriptive Statistics: Cluster 2 Sessions

Cluster 2 Sessions ^a
Variables	Mean	SD	Median	Max.	25th	75th
Duration$^{b}$	14.34	17.26	9.07	326.85	4.57	17.32
Bets	247	299	153	5,344	75	302
Intensity	17.26	3.17	17.81	21.91	14.77	20.03
Redeemed$^{c}$	54.98	94.35	30.00	2,450.00	10.00	50.00
Vouchers$^{c}$	44.06	412.79	.20	40,833.05	.00	20.00
Net Loss$^{c}$	42.74	90.52	19.75	2,450.00	4.60	50.00
%Loss	71.77	43.03	99.25	100.00	19.05	100.00
a. n = 14,496 b. Measured in minutes. c. Measured in Euros.

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Table 7. Descriptive Statistics: Cluster 3 Sessions

Cluster 3 Sessions ^a
Variables	Mean	SD	Median	Max.	25th	75th
Duration$^{b}$	782.53	160.19	780.66	1,099.72	686.27	899.45
Bets	246	524	86	6,715	39	227
Intensity	.32	.66	.12	7.25	.05	.31
Redeemed$^{c}$	52.34	127.88	20.00	1,530.00	10.00	50.00
Vouchers$^{c}$	.00	.00	.00	.00	.00	.00
Net Loss$^{c}$	52.34	127.88	20.00	1,530.00	10.00	50.00
%Loss	100.00	.00	100.00	100.00	100.00	100.00
a. n = 824 b. Measured in minutes. c. Measured in Euros.

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Table 8. Descriptive Statistics: Cluster 4 Sessions

Cluster 4 Sessions ^a
Variables	Mean	SD	Median	Max.	25th	75th
Duration$^{b}$	25.84	29.94	16.07	394.65	7.57	32.75
Bets	688	844	413	13,282	193	855
Intensity	26.12	4.00	25.35	68.32	23.59	27.43
Redeemed$^{c}$	116.87	218.11	50.00	6,425.00	20.00	120.00
Vouchers$^{c}$	97.40	499.21	.45	33,602.76	.20	100.00
Net Loss$^{c}$	78.06	193.43	19.90	6,424.69	.00	70.00
%Loss	58.59	45.74	97.32	100.00	.00	99.73
a. n = 17,253 b. Measured in minutes. c. Measured in Euros.

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Table 9. Between-Groups One-Way ANOVA

ANOVA
		Sum of Squares	df	Mean Square	F	Sig.
Duration	Between Groups	386.957	3	128.986	85580.335	.000
	Within Groups	69.952	46412	.002
	Total	456.909	46415
Intensity	Between Groups	847.409	3	282.470	110089.371	.000
	Within Groups	119.085	46412	.003
	Total	966.493	46415
Redeemed	Between Groups	1.542^*	3	.514	991.576	.000
	Within Groups	24.052	46412	.001
	Total	25.593	46415

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Table 10. Kruskal-Wallis Results

Ranks
	Cluster Membership	N	Mean Rank
Duration	1	13,843	28907.31
	2	14,496	16544.68
	3	824	46004.50
	4	17,253	23146.26
	Total	46,416
Intensity	1	13,843	7706.21
	2	14,496	21919.71
	3	824	1090.35
	4	17,253	37786.01
	Total	46,416
Redeemed	1	13,843	16455.75
	2	14,496	22349.74
	3	824	17893.58
	4	17,253	29601.97
	Total	46,416

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Table 11. Scatterdot: Cluster 1 Outlier Analysis

Descriptive Statistics: Cluster 1 Sessions (Normal v. Outliers)
	Normal				Outlier
	Mean	SD	Median	Count	Mean	SD	Median	Count
Duration	23.89	16.98		10815	130.20	67.56		3028
Bets	93	93	62		224	292	116
Intensity	4.67	3.02	4.22		1.99	2.37	.98
Redeemed	25.74	33.03	20.00		45.31	71.28	20.00
Voucher	2.02	26.37	.00		.08	4.37	.00
Net Loss	25.05	33.09	15.00		45.31	71.28	20.00
%Loss	96.63	17.67	100.00		99.97	1.82	100.00

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Table 12. Cluster 2 Sessions (Normal v. Outliers)

Descriptive Statistics: Cluster 2 Sessions (Normal v. Outliers)
	Normal				Outlier
	Mean	SD	Median	Count	Mean	SD	Median	Count
Duration	12.64	11.73		14198	95.54	34.68		298
Bets	218	209	149		1599	622	1461
Intensity	17.27	3.18	17.84		16.80	2.95	16.99
Redeemed	51.40	81.72	25.00		225.45	292.33	150.00
Voucher	44.46	415.10	.20		24.98	281.31	.00
Net Loss	39.02	76.65	19.65		219.91	294.73	147.50
%Loss	71.28	43.23	99.10		94.85	21.61	100.00

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Table 13. Cluster 4 Sessions (Normal v. Outliers)

Descriptive Statistics: Cluster 4 Sessions (Normal v. Outliers)
	Normal				Outlier
	Mean	SD	Median	Count	Mean	SD	Median	Count
Duration	23.04	21.79		16907	162.67	49.02		346
Bets	609	603	401		4519	1656	4071
Bets per Minute	26.09	3.99	25.32		27.65	4.58	26.88
Redeemed	106.51	173.11	50.00		623.16	805.07	350.00
Voucher	94.80	496.65	.45		224.20	598.56	.20
Net Loss	69.34	151.70	19.85		504.17	746.68	259.93
%Loss	59.55	45.81	97.00		72.47	40.45	99.97

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References

[1]	C. C. Aggarwal, Outlier Analysis, Springer, New York, 2013.
[2]	American Psychiatric Association, Diagnostic and Statistical Manual of Mental Disorders, 4^th edition, American Psychiatric Association, Washington, DC, 1994.
[3]	G. Banks, R. Fitzgerald and L. Sylvan, Gambling: Productivity Commission Inquiry Report, Technical Report 50,2010, http://www.pc.gov.au/inquiries/completed/gambling-2009/report/gambling-report-volume1.pdf(visited on: 09/12/2012).
[4]	M. Berry and G. Linoff, Data Mining Techniques for Marketing, Sales, and Customer Relationship Management, 2^nd edition, Wiley Publishing Inc., Indianapolis, 2004.
[5]	J. Braverman, R.A. LaBrie and H.J. Shaffer, A taxometric analysis of actual Internet sport gambling behavior, Psychological Assessment, 23 (2011), 234-244. doi: 10.1037/a0021404.
[6]	J. Braverman, D.A. LaPlante, S.E. Nelson and H.J. Shaffer, Using cross-game behavioral markers for early identification of high-risk Internet gamblers, Psychology of Addictive Behaviors, 27 (2013), 868-877. doi: 10.1037/a0032818.
[7]	J. Braverman and H.J. Shaffer, How do gamblers start gambling: Identifying behavioral markers for high-risk Internet gambling, European Journal of Public Health, 22 (2012), 273-278. doi: 10.1093/eurpub/ckp232.
[8]	S. Carpendale, Evaluating information visualizations, in Information Visualization, Lecture Notes in Computer Science, A simple univariate outlier identification procedure, 4950 (2008), 19-45. doi: 10.1007/978-3-540-70956-5_2.
[9]	National Research Council, Pathological Gambling: A Critical Review, National Academies Press, Washington D.C., 1999.
[10]	P. Delfabbro, A. Osborn, M. Nevile, L. Skelt and J. MacMillen, Identifying Problem Gamblers in Gambling Venues, Technical report, 2007.
[11]	M.J. Dixon, K.A. Harrigan, M. Jarrick, V. MacLaren, J.A. Fugelsang and E. Sheepy, Psychophysiological arousal signatures of near-misses in slot machine play, International Gambling Studies, 11 (2011), 393-407. doi: 10.1080/14459795.2011.603134.
[12]	L. Dixon, R. Trigg and M. Griffiths, An empirical investigation of music and gambling behaviour, International Gambling Studies, 7 (2007), 315-326. doi: 10.1080/14459790701601471.
[13]	S. Dragicevic, G. Tsogas and A. Kudic, Analysis of casino online gambling data in relation to behavioural risk markers for high-risk gambling and player protection, International Gambling Studies, 11 (2011), 377-391. doi: 10.1080/14459795.2011.629204.
[14]	M. Ellery, S.H. Stewart and P. Loba, Alcohol's effects on video lottery terminal (vlt) play among probable pathological and non-pathological gamblers, Journal of Gambling Studies, 21 (2005), 299-324. doi: 10.1007/s10899-005-3101-0.
[15]	J. Ferris and H. Wynne, The Canadian Problem Gambling Index: Final Report, Technical Report, 2001, http://www.ccgr.ca/en/projects/resources/CPGI-Final-Report-English.pdf(visited on: 06/28/2013).
[16]	G. Data, Canadian Gaming Market Report, Technical report, 2011, http://www.gamblingdata.com/files/Gambling%20Data%20Canadian%20Gaming%20Market%20Report%20Final_0.pdf (visited on: 04/10/2013).
[17]	GSA, G2S Message Protocol v1. 1 Game-to-system, Technical Report GSA-P0075. 024. 00-2011, GSA, 2011.
[18]	GSA, G2S Message Protocol v2. 0 Game-to-system, Technical Report GSA-P0075. 0800. 00-2006, GSA, 2006.
[19]	J. Han and M. Kamber, Data Mining: Concepts and Techniques, 3^rd edition, Morgan Kaufmann, Waltham, 2012.
[20]	K.A. Harrigan and M. Dixon, Par sheets, probabilities, and slot machine play: Implications of problem and non-problem gambling, Journal of Gambling Issues, 23 (2009), 81-110.
[21]	K.A. Harrigan, Slot machine structural characteristics: Distorted player views of payback percentages, Journal of Gambling Issues, 20 (2007), 215-234.
[22]	K.A. Harrigan, Slot machines: Pursuing responsible gaming practices for virtual reels and near misses, International Journal of Mental Health Addiction, 7 (2009), 68-83. doi: 10.1007/s11469-007-9139-8.
[23]	C. Hennig, Cluster-wise assessment of cluster stability, Computational Statistics & Data Analysis, 52 (2007), 258-271. doi: 10.1016/j.csda.2006.11.025.
[24]	D.C. Hoaglin, John W. Tukey and data analysis, Statistical Science, 18 (2003), 311-318. doi: 10.1214/ss/1076102418.
[25]	B. Iglewicz and S. Banerjee, A Simple Univariate Outlier Identification Procedure, Proceedings of Annual Meeting of the American Statistical Association, 2001.
[26]	R.A. LaBrie, D.A. LaPlante, S.E. Nelson, A. Schumann and H.J. Shaffer, Assessing the playing field: A prospective longitudinal study of Internet sports gambling behavior, Journal of Gambling Studies, 23 (2007), 347-362. doi: 10.1007/s10899-007-9067-3.
[27]	R.A. LaBrie, S.A. Kaplan, D.A. LaPlante, S.E. Nelson and H.J. Shaffer, Inside the virtual casino: A prospective longitudinal study of actual Internet casino gambling, European Journal of Public Health, 18 (2008), 410-416. doi: 10.1093/eurpub/ckn021.
[28]	D. A. LaPlante, S. E. Nelson, R. A. LaBrie and H. J. Shaffer, Stability and progression of disordered gambling: Lessons from longitudinal studies, Canadian Journal of Psychiatry, 53 (2008), 52-60. doi: 10.1177/070674370805300108.
[29]	D.A. LaPlante, S.E. Nelson, R.A. LaBrie and H.J. Shaffer, Disordered gambling, type of gambling and gambling involvement in the British gambling prevalence survey 2007, European Journal of Public Health, 21 (2011), 532-537. doi: 10.1093/eurpub/ckp177.
[30]	H. Liu and V. Keselj, Combined mining of web server logs and web contents for classifying user navigation patterns and predicting users' future requests, Data & Knowledge Engineering, 61 (2007), 304-330. doi: 10.1016/j.datak.2006.06.001.
[31]	P. Loba, S. H. Stewart, R. M. Klein and J. R. Blackburn, Manipulations of the features of standard video lottery terminal (VLT) games: Effects in pathological and non-pathological gamblers, Journal of Gambling Studies, 17 (2001), 94-98.
[32]	V.V. MacLaren, J.A. Fugelsang, K. Harrigan and M. Dixon, The personality of pathological gamblers: A meta-analysis, Clinical Psychology Review, 31 (2011), 1057-1067. doi: 10.1016/j.cpr.2011.02.002.
[33]	K. Marshall, Gambling 2011, Technical Report 4,2011, http://www.statcan.gc.ca/pub/75-001-x/2011004/article/11551-eng.pdf(visited on: 04/10/2013).
[34]	S. Mishra, M.L. LumiÃ©re and R.J. Williams, Gambling as a form of risk-taking: Individual differences in personality, risk-accepting attitudes, and behavioral preferences for risk, Personality and Individual Differences, 49 (2010), 616-621. doi: 10.1016/j.paid.2010.05.032.
[35]	National Research Council, Pathological Gambling: A Critical Review, The National Academies Press, Washington D.C., 1999.
[36]	S.R. Nelson, D.A. LaPlante, A.J. Peller, A. Schumann, R.A. LaBrie and H.J. Shaffer, Real limits in the virtual world: Self-limiting behavior of Internet gamblers, Journal of Gambling Studies, 24 (2008), 463-477. doi: 10.1007/s10899-008-9106-8.
[37]	J. Pallant, SPSS Survival Manual: A Step By Step Guide to Data Analysis Using SPSS, 4^th edition, Allen & Unwin, Sydney, 2011.
[38]	Y. Peng, K. Gang and Y. Shi (eds. ), Knowledge-rich data mining in financial risk detection, in Computational Science - ICCS 2009 (eds. G. Allen, J. Nabrzyski, E. Seidel, G. D. van Albada, J. Dongarra and P. M. A. Sloot), Springer Berlin Heidelberg, 5545 (2009), 534-542. doi: 10.1007/978-3-642-01973-9_60.
[39]	D. T. Pham, S. S. Dimov and C. D. Nguyen, Selection of k in k-means clustering, Journal of Mechanical Engineering Science, 219 (2005), 103-119. doi: 10.1243/095440605X8298.
[40]	A. Rakhlin and A. Caponnetto (eds. ), Stability of k-means clustering, in Advances in Neural Information Processing Systems 19 (eds. B. Schölkopf, J. Platt and T. Hoffman), MIT Press, (2006), 1121-1128. http://papers.nips.cc/paper/3116-stability-of-k-means-clustering (visited on: 12/10/2014)
[41]	Responsible Gambling Council, Electronic Gaming Machines and Problem Gambling, Saskachewan Liquour and Gaming Authority, 2006, http://www.responsiblegambling.org/docs/research-reports/electronic-gaming-machines-and-problem-gambling.pdf?sfvrsn=10 (visited on: 06/28/2013).
[42]	Responsible Gambling Council, Canadian Gambling Digest 2011-2012, Technical report, 2013, http://www.responsiblegambling.org/docs/default-document-library/20130605_canadian_gambling_digest_2011-12.pdf?sfvrsn=2 (visited on: 05/04/2015).
[43]	G. Schwartz, The Impulse Economy, Atria Books, New York, 2011.
[44]	S. Seo, A Review and Comparison of Methods for Detecting Outliers in Univariate Data Sets, M. S thesis, University of Pittsburg in Pensylvania, 2006.
[45]	H.J. Shaffer and D.A. Korn, Gambling and related mental disorders: A public health analysis, Annual Review of Public Health, 23 (2002), 171-212. doi: 10.1146/annurev.publhealth.23.100901.140532.
[46]	H.J. Shaffer, A.J. Peller, D.A. LaPlante, S.E. Nelson and R.A. LaBrie, Toward a paradigm shift in Internet gambling research: From opinion and self-report to actual behavior, Addiction Research and Theory, 18 (2010), 270-283. doi: 10.3109/16066350902777974.
[47]	J. Sim and C.C. Wright, Understanding interobserver agreement: The Kappa statistic, Family Medicine, 37 (2005), 360-363.
[48]	S. H. Stewart, P. Collins, J. R. Blackburn, M. Ellery and R. M. Klein, Heart rate increase to alcohol administration and video lottery terminal (VLT) play among regular VLT players, Psychology of Addictive Behaviors, 19 (2005), 94-98. doi: 10.1037/0893-164X.19.1.94.
[49]	S. Tufféry, Data Mining and Statistics for Decision Making, John Wiley & Sons, Ltd., Chichester, 2011. doi: 10.1002/9780470979174.
[50]	A.J. Viera and J.M. Garrett, The Kappa statistic in reliability studies: Use, interpretation, and sample size requirements, Journal of the American Physical Therapy Association, 85 (2005), 257-268.
[51]	C. Wheelan, Naked Statistics: Stripping the Dread from the Data, W. W. Norton and Company, New York, 2013.
[52]	R. J. Williams, R. A. Volberg and R. M. G. Stevens, The Population Prevalence of Problem Gambling: Methodological Influences, Standardized Rates, Jurisdictional Differences, and Worldwide Trends, Technical report, 2012, https://www.uleth.ca/dspace/bitstream/handle/10133/3068/2012-PREVALENCE-OPGRC%20(2).pdf?sequence=3 (visited on: 08/12/2013).
[53]	D. S. Wilson, R. A. Kauffman and M. S. Purdy, A program for at-risk high school students informed by evolutionary science, PLoS ONE, 31 (2002), 76-77. doi: 10.1371/journal.pone.0027826.
[54]	I.H. Witten and E. Frank, Data mining: Practical machine learning tools and techniques, Newsletter: ACM SIGMOD Record Homepage archive, 31 (2002), 76-77. doi: 10.1145/507338.507355.
[55]	Z. Xuan and H. Shaffer, How do gamblers end gambling: Longitudinal analysis of Internet gambling behaviors prior to account closure due to gambling related problems, Journal of Gambling Studies, 25 (2009), 239-252. doi: 10.1007/s10899-009-9118-z.