Databases Reference
In-Depth Information
Shim [NRS99]; and Zaıane, 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 topics 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
C
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
C
08]).
Books and surveys on stream data systems and stream data processing include Babu
and Widom [BW01]; Babcock, Babu, Datar, et al. [BBD
C
02]; Muthukrishnan [Mut05];
and Aggarwal [Agg06].
Stream data mining
research covers stream cube models (e.g., Chen, Dong, Han,
et al. [CDH
C
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 topics 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 exam-
ple, topics by Berry and Linoff [BL04] and Berson, Smith, and Thearling [BST99]. For
telecommunication-related data mining, see, for example, Horak [Hor08]. There are
also topics on scientific data analysis, such as Grossman, Kamath, Kegelmeyer, et al.
[GKK
C
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 Barbar a, DuMouchel, Faloutos, et al. [BDF
C
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 deci-
sion 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
