13.8 Bibliographic Notes

For mining complex data types, there are many research papers and books covering various themes. We list here some recent books and well-cited survey or research articles for references.

Time-series analysis has been studied in statistics and computer science communities for decades, with many textbooks such as Box, Jenkins, and Reinsel [BJR08]; Brockwell and Davis [BD02]; Chatfield [Cha03b]; Hamilton [Ham94]; and Shumway and Stoffer [SS05]. A fast subsequence matching method in time-series databases was presented by Faloutsos, Ranganathan, and Manolopoulos [FRM94]. Agrawal, Lin, Sawhney, and Shim [ALSS95] developed a method for fast similarity search in the presence of noise, scaling, and translation in time-series databases. Shasha and Zhu present an overview of the methods for high-performance discovery in time series [SZ04].

Sequential pattern mining methods have been studied by many researchers, including Agrawal and Srikant [AS95]; Zaki [Zak01]; Pei, Han, Mortazavi-Asl, et al. [PHM-A⁺04]; and Yan, Han, and Afshar [YHA03]. The study on sequence classification includes Ji, Bailey, and Dong [JBD05] and Ye and Keogh [YK09], with a survey by Xing, Pei, and Keogh [XPK10]. Dong and Pei [DP07] provide an overview on sequence data mining methods.

Methods for analysis of biological sequences including Markov chains and hidden Markov models are introduced in many books or tutorials such as Waterman [Wat95]; Setubal and Meidanis [SM97]; Durbin, Eddy, Krogh, and Mitchison [DEKM98]; Baldi and Brunak [BB01]; Krane and Raymer [KR03]; Rabiner [Rab89]; Jones and Pevzner [JP04]; and Baxevanis and Ouellette [BO04]. Information about BLAST (see also Korf, Yandell, and Bedell [KYB03]) can be found at the NCBI web site www.ncbi.nlm.nih.gov/BLAST/.

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, 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, 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 Chapters 10 and 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, and Zhang [HLZ02].

Text data analysis has been studied extensively in information retrieval, with many textbooks and survey articles such as Croft, Metzler, and Strohman [CMS09]; S. Buttcher, C. Clarke, G. Cormack [BCC10]; Manning, Raghavan, and Schutze [MRS08]; Grossman and Frieder [GR04]; Baeza-Yates and Riberio-Neto [BYRN11]; Zhai [Zha08]; Feldman and Sanger [FS06]; Berry [Ber03]; and Weiss, Indurkhya, Zhang, and Damerau [WIZD04]. Text mining is a fast-developing field with numerous papers published in recent years, covering many topics such as topic models (e.g., Blei and Lafferty [BL09]); sentiment analysis (e.g., Pang and Lee [PL07]); and contextual text mining (e.g., Mei and Zhai [MZ06]).

Web mining is another focused theme, with books like Chakrabarti [Cha03a], Liu [Liu06], and Berry [Ber03]. Web mining has substantially improved search engines with a few influential milestone works, such as Brin and Page [BP98]; Kleinberg [Kle99]; Chakrabarti, Dom, Kumar, et al. [CDK⁺99]; and Kleinberg and Tomkins [KT99]. Numerous results have been generated since then, such as search log mining (e.g., Silvestri [Sil10]); blog mining (e.g., Mei, Liu, Su, and Zhai [MLSZ06]); and mining online forums (e.g., Cong, Wang, Lin, et al. [CWL⁺08]).

Books and surveys on stream data systems and stream data processing include Babu and Widom [BW01]; Babcock, Babu, Datar, et al. [BBD⁺02]; Muthukrishnan [Mut05]; and Aggarwal [Agg06].

Stream data mining research covers stream cube models (e.g., Chen, Dong, Han, et al. [CDH⁺02]), stream frequent pattern mining (e.g., Manku and Motwani [MM02] and Karp, Papadimitriou and Shenker [KPS03]), stream classification (e.g., Domingos and Hulten [DH00]; Wang, Fan, Yu, and Han [WFYH03]; Aggarwal, Han, Wang, and Yu [AHWY04b]), and stream clustering (e.g., Guha, Mishra, Motwani, and O’Callaghan [GMMO00] and Aggarwal, Han, Wang, and Yu [AHWY03]).

There are many books that discuss data mining applications. For financial data analysis and financial modeling, see, for example, Benninga [Ben08] and Higgins [Hig08]. For retail data mining and customer relationship management, see, for example, books by Berry and Linoff [BL04] and Berson, Smith, and Thearling [BST99]. For telecommunication-related data mining, see, for example, Horak [Hor08]. There are also books on scientific data analysis, such as Grossman, Kamath, Kegelmeyer, et al. [GKK⁺01] and Kamath [Kam09].

Issues in the theoretical foundations of data mining have been addressed by many researchers. For example, Mannila presents a summary of studies on the foundations of data mining in [Man00]. The data reduction view of data mining is summarized in The New Jersey Data Reduction Report by Barbará, DuMouchel, Faloutos, et al. [BDF⁺97]. The data compression view can be found in studies on the minimum description length principle, such as Grunwald and Rissanen [GR07].

The pattern discovery point of view of data mining is addressed in numerous machine learning and data mining studies, ranging from association mining, to decision tree induction, sequential pattern mining, clustering, and so on. The probability theory point of view is popular in the statistics and machine learning literature, such as Bayesian networks and hierarchical Bayesian models in Chapter 9, and probabilistic graph models (e.g., Koller and Friedman [KF09]). Kleinberg, Papadimitriou, and Raghavan [KPR98] present a microeconomic view, treating data mining as an optimization problem. Studies on the inductive database view include Imielinski and Mannila [IM96] and de Raedt, Guns, and Nijssen [RGN10].

Statistical methods for data analysis are described in many books, such as Hastie, Tibshirani, Friedman [HTF09]; Freedman, Pisani, and Purves [FPP07]; Devore [Dev03]; Kutner, Nachtsheim, Neter, and Li [KNNL04]; Dobson [Dob01]; Breiman, Friedman, Olshen, and Stone [BFOS84]; Pinheiro and Bates [PB00]; Johnson and Wichern [JW02b]; Huberty [Hub94]; Shumway and Stoffer [SS05]; and Miller [Mil98].

For visual data mining, popular books on the visual display of data and information include those by Tufte [Tuf90, Tuf97, Tuf01]. A summary of techniques for visualizing data is presented in Cleveland [Cle93]. A dedicated visual data mining book, Visual Data Mining: Techniques and Tools for Data Visualization and Mining, is by Soukup and Davidson [SD02]. The book Information Visualization in Data Mining and Knowledge Discovery, edited by Fayyad, Grinstein, and Wierse [FGW01], contains a collection of articles on visual data mining methods.

Ubiquitous and invisible data mining has been discussed in many texts including John [Joh99], and some articles in a book edited by Kargupta, Joshi, Sivakumar, and Yesha [KJSY04]. The book Business @ the Speed of Thought: Succeeding in the Digital Economy by Gates [Gat00] discusses e-commerce and customer relationship management, and provides an interesting perspective on data mining in the future. Mena [Men03] has an informative book on the use of data mining to detect and prevent crime. It covers many forms of criminal activities, ranging from fraud detection, money laundering, insurance crimes, identity crimes, and intrusion detection.

Data mining issues regarding privacy and data security are addressed popularly in literature. Books on privacy and security in data mining include Thuraisingham [Thu04]; Aggarwal and Yu [AY08]; Vaidya, Clifton, and Zhu [VCZ10]; and Fung, Wang, Fu, and Yu [FWFY10]. Research articles include Agrawal and Srikant [AS00]; Evfimievski, Srikant, Agrawal, and Gehrke [ESAG02]; and Vaidya and Clifton [VC03]. Differential privacy was introduced by Dwork [Dwo06] and studied by many such as Hay, Rastogi, Miklau, and Suciu [HRMS10].

There have been many discussions on trends and research directions of data mining in various forums. Several books are collections of articles on these issues such as Kargupta, Han, Yu, et al. [KHY⁺08].