[ AnkerstM, BreunigM, K rieg elH P, et a.l OPT ICS: order ing po ints to identify the cluster ing structure[ C] / / Proceed ing s o f the 1999 Intl Con fM anag em ent of Data. New York: ACM, 1999: 49-60.
[2]
[ H inneburg A, Ke im D A. An effic ient approach to cluster ing in largem ultim edia da tabases w ith no ise[ C ] / / Proceed ings o f the 1998 Intl Con fKnow ledge Discovery and DataM in ing. San Franc isco: M org an Kau fm ann, 1998: 58-65.
[3]
[ W angW, Yang J, M un tz R. STING: a statistica l in fo rm ation gr id approach to spatia l da tam in ing [ C ] / / Proceed ings o f the 1997 Intl Con f Very La rge Data Bases. San Franc isco: M organ Kaufm ann, 1997: 186-195.
[4]
[ She ikho leslam iG, Chatterjee S, ZhangA. W aveC luster: a mu ltireso lution c luste ring approach fo r very large spatia l databases [ C ] / / Proceed ings of the 1998 Intl Conf Very Larg e Da ta Bases. San Franc isco: M organ Kaufm ann, 1998: 428-439.
[5]
[ Ag raw al R, G ehrke J, Gunopulos D, et a .l Autom atic subspace cluster ing of high d im ensiona l data for data m ining app lica ？tions[ C] / / Proceedings o f the 1998 ACM SIGMOD. N ew York: ACM, 1998: 94-105.
[6]
[ Dem pste rA, La ird N, Rub in D. M ax im um likelihood from incomp le te data v ia the EM algor ithm [ J] . J Roya l Sta tistica l So ci ？ety, 1977, 39: 1-38.
[7]
[ Genna ri J, Lang ley P, Fisher D. M ode ls o f increm enta l concept form ation[ J]. Artificia l Inte lligence, 1989, 40: 11-61.
[8]
[ Kohonen T. Self o rganized form a tion o f topo log ica lly correc t featurem aps[ J]. B io log ical Cybernetics, 1982, 43: 59-69.
[9]
[ Tung A K H, Hou J, H an J. Spatia l c lustering in the presence o f obstac les[ C] / /Proceed ing s of the 2001 ICDE. W ashing ton DC: IEEE Com pute r Society, 2001: 359-367.
[10]
[ E stiv ill C astroV, Lee I. Autoclust+ : autom a tic cluster ing o f point??da ta sets in the presence o f obstac les[ C] / /Proceed ing s o f the 2000 TSDM. London: Spr inge r？ Ver lag, 2000: 133-146.
[11]
[ Za ianeO R, Lee C H. C luster ing spatial data in the presence of obstac les: A density??based approach[ C] / /Pro ceedings o f the 2002 IDEAS. W ash ing ton DC: IEEE Computer Soc ie ty, 2002: 214-223.
[12]
[ W ang X, Rostoker C, H am ilton H J. Density based spa tia l cluster ing in the presence o f obstac les and facilita to rs[ C ] / /Pro ceed ing s of the 2004 PKDD. New York: Springer Verlag, 2004: 446-458
[13]
[ V lad im irE C, Lee I. C luster ing w ith obstacles for geog raph ica l data m in ing[ J] . ISPRS Journa l of H otogramm e try & Rem ote Sensing, 2004, 59: 21-34.
[14]
[ Shash i S, Y an H. D iscover ing spatia l co location patterns: a summa ry of results[ C ] / / Proc of the Seventh Internationa lSympo sium on Spa tia l and Temporal Databases. London: Spr inge r Ver lag, 2001: 236-256.
[15]
[ Shash i S, San jay C. 空间数据库[M ]. 谢昆青, 马修军, 杨冬青, 译. 北京: 机械工业出版社, 2004: 214-215. Shash i S, San jay C. Spa tia l Da tabases A Tour[M ] . X ie Kunqing, M a X iu jun, Yang Dongq ing, Translated. B eijing: Ch ina M a?? ch ine Press, 2004: 214-215. ( in Chinese)
[16]
[ H an J, Lee J, Kam be rM. Geographic Da taM in ing and Know ledge D iscove ry[M ]. 2nd ed. Boca Raton: Tay lo r and Franc is, 2009: 149-152.
[17]
[ Andrew A, Thomas C, Korniss G. Eco log ica l invasion: spa tia l c lustering and the cr itica l radius[ J]. E vo lutiona ry Eco logy Re ？search, 2007, 9: 375-394.
[18]
[ W an L, LiY, LiuW, et a.l App lication and study of spatial cluster and custom er partition ing [ C] / / Proceed ing s of theFou rth Interna tiona l Conference onM achine Learn ing and Cybe rnetics. Guang zhou: IEEE, 2005: 1 701-1 706.
[19]
[ Sander J, EsterM, K riege lH P, e t a.l Density Based C lustering in Spatial Databases: The A lgo rithm GDBSCAN and Its Ap ？p lications, Da taM ining and Know ledge D iscov ery[M ]. Ne ther lands: K luw er Academ ic Press, 1998: 169-194.
[20]
[ Zhang Q, Cou lo igner I. A new and e fficient k m edo id algorithm for spatial c lustering [ C ] / / Proceed ing s o f the 2005 ICCSA. S ing apore: Spr ing er Ve rlag, 2005: 181-189.
[21]
[ Borah B, Bhattacharyya D K. DDSC: a dens ity d ifferentiated spatia cluster ing technique[ J] . Journa l o f Com puters, 2008, 2: 72-79.
[22]
[ JiG, M iao J, Bao P A. Spatial c luste ring a lgor ithm based on spa tia l topo log ica l relations forGML data[ C ] / / Pro ceedings o f In tl Conf on A rtific ial Intelligence and Compu tational Intelligence. Shangha:i IEEE Com pute r Society, 2009: 298-301.
[23]
[ Ji G, M iao J, Y angM. A nove l spa tia l cluster ing a lgo rithm based on spatial ad jacent re lations for GM L data[ C] / / Proceed ？ing s of IntlWo rkshop on Educa tion Techno logy and Compu ter Sc ience. Wuhan: IEEE Com puter Soc iety, 2009: 278-281.
[24]
[ Ji G, Zhang L A. Spatial po lygon ob jec ts c lustering a lgor ithm based on topo log ical re la tions for GML data[ C] / / Proceed ing s of Intl Conference on Inform ation Eng inee ring and Compu ter Sc ience. Wuhan: IEEE Press, 2009: 363-366.
[25]
[ Yang N, J iG A. Spa tia l lines cluster ing a lgo rithm based on adjacen t re lations for GM L data[ C ] / / Proceedings o f Int Conf on In fo rm ation Eng ineer ing and Com puter Science. W uhan: IEEE Press, 2009: 3 593-3 596.
[26]
[ H an J, K amberM. 数字挖掘概念与技术[M ]. 范明, 孟晓峰, 译. 北京: 机械工业出版社, 2002: 224-225. H an J, KamberM. Data M ing ing Concepts and Techniques[M ]. Fan M ing, M eng X iao feng, Translated. Be ijing: China M a ？ch ine Press, 2002: 224-225. ( in Chinese)
[27]
[ L loyd S P. Least squa res quantization in PCM [ J]. IEEE Trans In fo rm ation Theory, 1982, 28: 128-137.
[28]
[ K aufm an L, Rousseeuw P J. F inding Groups in Data: An Introduc tion to C luster Analysis[M ]. New Yo rk: JohnW iley& Sons, 1990.
[29]
[ Ng A R, H an J. E ffic ient and effective cluster ing m ethod for spatial da tam ining[ C] / /Proceed ings of the 1994 Intl Con fVery Large Da tabases. San Franc isco: M org an Kau fm ann, 1994: 144-155.
[30]
[ Zhang T, Ram akrishnan R, L ivnyM. BIRCH: an e fficien t data cluster ingm ethod for ve ry la rge databases[ C] / / Proceed ing s of the 1996 Intl ConfM anagem ent o f Data. New Yo rk: ACM, 1996: 103-114.
[31]
[ Guha S, Rastog iR, Sh im K. Cure: An e fficient c lustering a lgor ithm for larg e databases[ C ] / / Proceedings o f the 1998 ACM ？S IGMOD Int Con fM anagem en t of Data. New York: ACM, 1998: 73-84.
[32]
[ Guha S, Rastog iR, Sh im K. ROCK: a robust c lustering algor ithm fo r categor ica l attr ibutes[ C] / / Proceed ings of the 1999 Int Conf Data Eng ineering. W ash ington DC: IEEE Com puter So ciety, 1999: 512-521.
[33]
[ Karyp is G, H an E H, Kuma rV. CHAMELEON: a hierarch ica l c lustering a lgor ithm using dynam icm ode ling[ J] . Compu ter, 1999, 32: 68-75.
[34]
[ EsterM, K rieg elH P, Sander J, et a.l A density based a lgor ithm fo r discovering clusters in large spatia l databases[ C ] / / Pro ceedings of the 1996 Intl Conf Know ledge D iscov ery and DataM in ing. Am ste rdam: E lsev ier Sc ience, 1996: 226-231.
