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Tuf90, Tuf97 and 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].

Bibliography

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[AAP01] Agarwal, R.; Aggarwal, C.C.; Prasad, V.V.V., A tree projection algorithm for generation of frequent itemsets, J. Parallel and Distributed Computing 61 (2001) 350–371.

[AB79] Abraham, B.; Box, G.E.P., Bayesian analysis of some outlier problems in time series, Biometrika 66 (1979) 229–248.

[AB99] Albert, R.; Barabasi, A.-L., Emergence of scaling in random networks, Science 286 (1999) 509–512.

[ABA06] Agyemang, M.; Barker, K.; Alhajj, R., A comprehensive survey of numeric and symbolic outlier mining techniques, Intell. Data Anal 10 (2006) 521–538.

[ABKS99] Ankerst, M.; Breunig, M.; Kriegel, H.-P.; Sander, J., OPTICS: Ordering points to identify the clustering structure, In: Proc. 1999 ACM-SIGMOD Int. Conf. Management of Data (SIGMOD’99) Philadelphia, PA. (June 1999), pp. 49–60.

[AD91] Almuallim, H.; Dietterich, T.G., Learning with many irrelevant features, In: Proc. 1991 Nat. Conf. Artificial Intelligence (AAAI’91) Anaheim, CA. (July 1991), pp. 547–552.

[AEEK99] Ankerst, M.; Elsen, C.; Ester, M.; Kriegel, H.-P., Visual classification: An interactive approach to decision tree construction, In: Proc. 1999 Int. Conf. Knowledge Discovery and Data Mining (KDD’99) San Diego, CA. (Aug. 1999), pp. 392–396.

[AEMT00] Ahmed, K.M.; El-Makky, N.M.; Taha, Y., A note on “beyond market basket: Generalizing association rules to correlations.”, SIGKDD Explorations 1 (2000) 46–48.

[AG60] Anscombe, F.J.; Guttman, I., Rejection of outliers, Technometrics 2 (1960) 123–147.

[Aga06] Agarwal, D., Detecting anomalies in cross-classified streams: A Bayesian approach, Knowl. Inf. Syst 11 (2006) 29–44.

[AGAV09] Amigó, E.; Gonzalo, J.; Artiles, J.; Verdejo, F., A comparison of extrinsic clustering evaluation metrics based on formal constraints, Information Retrieval 12 (4) (2009) 461–486.

[Agg06] Aggarwal, C.C., Data Streams: Models and Algorithms. (2006) Kluwer Academic .

[AGGR98] Agrawal, R.; Gehrke, J.; Gunopulos, D.; Raghavan, P., Automatic subspace clustering of high dimensional