H. J. Miller and J. Han, Geographic data mining and knowledge discovery: An overview, In H. J. Miller and J. Han (Eds.) Geographic Data Mining and Knowledge Discovery, London: Taylor and Francis, pp. 3-32, 2001.  H. J. Miller, Geographic data mining and knowledge discovery, In J. P. Wilson and A. S. Fotheringham (Eds.) Handbook of Geographic Information Science, ISBN: 978-1-4051-0795-2, article No 19, 2007.  D. Guo, Multivariate spatial clustering and geovisualization. In Geographic Data Mining and Knowledge Discovery, In H. J. Miller and J. Han (Eds.). London and New York: Taylor & Francis, pp. 325-345, 2009.  J. Han, M. Kamber and A.K.H. Tung. Spatial clustering methods in data mining: A survey, In: Geographic Data Mining and Knowledge Discovery. H.J. Miller and J. Han, (eds.), London: Taylor & Francis, pp. 33–50, 2001.  J. Han, K. Koperski and N. Stefanovic, GeoMiner: A system prototype for spatial data mining, ACM SIGMOD International Conference on Management of Data, Tucson, AZ, pp. 553–556, 1997.  S. Shekhar, C.T. Lu and P. Zhang, A unified approach to detecting spatial outliers, GeoInformatica, 7, pp. 139–166, 2003.  H. Chen, W. Chung, J.J. Xu., G. Wang, Y.Qin and M. Chau, Crime data mining: A general framework and some examples, University of Arizona; published by IEEE Computer Society Press Los Alamitos, CA, USA, 2004.  H. Chen, W. Chung, Y.Qin, M.Chau, J.J.Xu, G.Wang, R. Zheng and H. Atabakhsh, Crime data mining: An overview and case studies, 2003.  H. Chen, H. Atabakhsh, T. Petersen, J. Schroeder, T. Buetow, L. Chaboya, C.O’Toole, M.Chau, T.Cushna, D. Casey and Z. Huang, COPLINK: Visualization for crime analysis, Proc. of The National Conf. on Digital Government Research, 2003.  Y. Xiang, M. Chau, H. Atabakhsh and H.Chen, Visualizing criminal relationships: Comparison of a hyperbolic tree and a hierarchical list, University of Arizona, 2004.  P. Thongtae and S. Srisuk, An analysis of data mining applications in crime domain, citworkshops, pp. 122-126, IEEE 8th International Conf. on Computer and Information Technology Workshops, 2008.  A.Gonzales, R.Schofield, and S.Hart, Mapping crime: Understanding hotspot. U.S. Department of Justice, 2005.  M. Ahmadi, A Sharifi and M.J. Valadan, Crime mapping and spatial analysis, International institute for geo-information science and earth observation, Enschede, Neatherlands, 2003.  V.Estivill-Castro and I. Lee, Data mining techniques for autonomous exploration of large volumes of geo-referenced crime data, 6th Int. Conf. on Geocomputation, Brisbane, Australia, 2008.
 M.Wyland, Design and Implementation of a spatial Data Engine and Visualization Interface for a Crime Information System, 2008.
 L.Kelvin, C.Stephen, N.Vincent and S.Simon, Introduction of STEM: Space-Time-Event Model for crime pattern analysis. Asian journal of information technology, 2008.  M.A.Santos da Silva, A.M. Vieira Monteiro and J.S. Medeiros, Visualization of Geospatial data by component plane and U-Matrix, Brazil, 2008.
 L.Kelvin, J.Li, C. Stephen and N.Vincent, An Application of the dynamic pattern analysis framework to the analysis of spatial-temporal crime relationships, Journal of Universal Computer Science, vol. 15, no. 9, 2009.  R.W.Adderley, The use of data mining techniques in crime trend analysis and offender, profiling, PhD thesis, Publisher: University of Wolverhampton, 2007.  N. Levin, The CrimeStat Program: Characteristics, Use, and Audience, Houston, TX, 2004  P. Mohan, S. Shekhar, N. Levine, R. Wilson, B. George and M.Celik, Should SDBMS support a join index?: A case study from crime stat, USA(c) 2008 ACM, ISBN:978-1-60558-323-5, 2008.  A. Helmstetter and D. Sornette, Subcritical and supercritical regimes in epidemic models of earthquake aftershocks, J. Geophys. Res., 107(B10), 2237, DOI:10.1029/2001JB001580, 2002.  Y.Y. Kagan and L.Knopoff, Statistical short-term earthquake prediction, Science 236, pp. 1563–1567, 1987.  Y.Ogata, Statistical models for earthquake occurrence and residual analysis for point processes, J. Am. stat. Assoc., 83, pp. 9-27, 1998.  W.Dzwinel, D.A.Yuen, K.Boryczko, Y.Ben-Zion, S. Yoshioka and T.Ito, Cluster analysis, data-mining, multi-dimensional visualization of earthquakes over space, time and feature space, Nonlinear Processes in Geophysics. Vol. 12. pp. 117-128, 2005.  C.C.Chen, J. B.Rundle, J. R.Holliday, K. Z.Nanjo, D. L.Turcotte, S.C. Li and K. F.Tiampo, The 1999 Chi-Chi, Taiwan, earthquake as a typical example of seismic activation and quiescence, Geophys. Res. Lett., 32, L22315, DOI:10.1029/ 2005GL023991, 2005.  R.Muir-Wood, Earthquake clustering due to stress interactions, proceedings of the 2008 science symposium: Advances in Earthquake Forcasting, RMS Special Report 2008, Risk Management Solutions,Inc, 2008.
Journal of Computer and Robotics 1 (2010) 53-67
 M.R.Keyvanpour, M.Javideh, M.R. Ebrahimi, and M.Sojoodi, Using Geographical information systems for crime prevention, Proceedings of National Conf. on Crime Prevention, Iran, 2008.  G.C.Oatley, B.W.Ewart and J.Zeleznikow, Decision support systems for police: lessons from the application of data mining techniques to 'Soft' forensic evidence, Journal of Artificial Intelligence and Law, Vol. 14, No. 1-2, DOI: 10.1007/s10506-006-9023-z, 2006.  http://www.crimereduction.homeoffice.gov.uk.  J.Reno, D.Marcus, L.Robinson, N.Brennan, and J.Travis, Mapping crime principle and practice, U.S. Department of Justice, 1999.
 J.Han, and M.Kamber, Data mining concepts and techniques, second edition, Morgan Kaufmann, November 3, 2005.
 G.K. Gupta, Introduction to data mining with case studies, prentice-hall of India, New Delhi, 2006.
 X.W. Syrmos, Optimal cluster selection based on Fisher class separability measure, American Control Conference, IEEE, 2005.
 B.Raskutti and C.Leckie, An evaluation of criteria for measuring the quality of clusters, pp. 905 – 910, ISBN:1-55860-613-0, Morgan Kaufmann Publishers Inc. San Francisco, CA, USA, 1999.