Data Mining_ Concepts and Techniques - Jiawei Han [380]
Graph pattern mining has been studied extensively, including Holder, Cook, and Djoko [HCD94]; Inokuchi, Washio, and Motoda [IWM98]; Kuramochi and Karypis [KK01]; Yan and Han YH02 and YH03a; Borgelt and Berthold [BB02]; Huan, Wang, Bandyopadhyay, et al. [HWB+04]; and the Gaston tool by Nijssen and Kok [NK04].
There has been a great deal of research on social and information network analysis, including Newman [New10]; Easley and Kleinberg [EK10]; Yu, Han, and Faloutsos [YHF10]; Wasserman and Faust [WF94]; Watts [Wat03]; and Newman, Barabasi, and Watts [NBW06]. Statistical modeling of networks is studied popularly such as Albert and Barbasi [AB99]; Watts [Wat03]; Faloutsos, Faloutsos, and Faloutsos [FFF99]; Kumar, Raghavan, Rajagopalan, et al. [KRR+00]; and Leskovec, Kleinberg, and Faloutsos [LKF05]. Data cleaning, integration, and validation by information network analysis was studied by many, including Bhattacharya and Getoor [BG04] and Yin, Han, and Yu YHY07 and YHY08.
Clustering, ranking, and classification in networks has been studied extensively, including in Brin and Page [BP98]; Chakrabarti, Dom, and Indyk [CDI98]; Kleinberg [Kle99]; Getoor, Friedman, Koller, and Taskar [GFKT01]; Newman and M. Girvan [NG04]; Yin, Han, Yang, and Yu [YHYY04]; Yin, Han, and Yu [YHY05]; Xu, Yuruk, Feng, and Schweiger [XYFS07]; Kulis, Basu, Dhillon, and Mooney [KBDM09]; Sun, Han, Zhao, et al. [SHZ+09]; Neville, Gallaher, and Eliassi-Rad [NGE-R09]; and Ji, Sun, Danilevsky et al. [JSD+10]. Role discovery and link prediction in information networks have been studied extensively as well, such as by Krebs [Kre02]; Kubica, Moore, and Schneider [KMS03]; Liben-Nowell and Kleinberg [L-NK03]; and Wang, Han, Jia, et al. [WHJ+10].
Similarity search and OLAP in information networks has been studied by many, including Tian, Hankins, and Patel [THP08] and Chen, Yan, Zhu, et al. [CYZ+08]. Evolution of social and information networks has been studied by many researchers, such as Chakrabarti, Kumar, and Tomkins [CKT06]; Chi, Song, Zhou, et al. [CSZ+07]; Tang, Liu, Zhang, and Nazeri [TLZN08]; Xu, Zhang, Yu, and Long [XZYL08]; Kim and Han [KH09]; and Sun, Tang, and Han [STH+10].
Spatial and spatiotemporal data mining has been studied extensively, with a collection of papers by Miller and Han [MH09] and was introduced in some textbooks, such as Shekhar and Chawla [SC03] and Hsu, Lee, and Wang [HLW07]. Spatial clustering algorithms have been studied extensively in Chapter 10 and Chapter 11 of this book. Research has been conducted on spatial warehouses and OLAP, such as by Stefanovic, Han, and Koperski [SHK00] and spatial and spatiotemporal data mining, such as by Koperski and Han [KH95]; Mamoulis, Cao, Kollios, Hadjieleftheriou, et al. [MCK+04]; Tsoukatos and Gunopulos [TG01]; and Hadjieleftheriou, Kollios, Gunopulos, and Tsotras [HKGT03]. Mining moving-object data has been studied by many, such as Vlachos, Gunopulos, and Kollios [VGK02]; Tao, Faloutsos, Papadias, and Liu [TFPL04]; Li, Han, Kim, and Gonzalez [LHKG07]; Lee, Han, and Whang [LHW07]; and Li, Ding, Han, et al. [LDH+10]. For the bibliography of temporal, spatial, and spatiotemporal data mining research, see a collection by Roddick, Hornsby, and Spiliopoulou [RHS01].
Multimedia data mining has deep roots in image processing and pattern recognition, which have been studied extensively in many textbooks, including Gonzalez and Woods [GW07]; Russ [Rus06]; Duda, Hart, and Stork [DHS01]; and Z. Zhang and R. Zhang [ZZ09]. Searching and mining of multimedia data has been studied by many (see, e.g., Fayyad and Smyth [FS93]; Faloutsos and Lin [FL95]; Natsev, Rastogi, and Shim [NRS99]; and Zaïane, Han, and Zhu [ZHZ00]). An overview of image mining methods is given by Hsu, Lee,