Databases Reference
In-Depth Information
as Bayesian networks and hierarchical Bayesian models in Chapter 9, and probabilis-
tic graph models (e.g., Koller and Friedman [KF09]). Kleinberg, Papadimitriou, and
Raghavan [KPR98] present a microeconomic view, treating data mining as an optimiza-
tion 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 topics, 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 topics 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 topic,
Visual
Data Mining: Techniques and Tools for Data Visualization and Mining
, is by Soukup and
Davidson [SD02]. The topic
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 topic edited by Kargupta, Joshi, Sivakumar, and
Yesha [KJSY04]. The topic
Business @ the Speed of Thought: Succeeding in the Digital
Economy
by Gates [Gat00] discusses e-commerce and customer relationship manage-
ment, and provides an interesting perspective on data mining in the future. Mena
[Men03] has an informative topic 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 min-
ing
in various forums. Several topics are collections of articles on these issues such as
Kargupta, Han, Yu, et al. [KHY
C
08].
