Data Mining_ Concepts and Techniques - Jiawei Han [379]
13.9 Propose a few implementation methods for audio data mining. Can we integrate audio and visual data mining to bring fun and power to data mining? Is it possible to develop some video data mining methods? State some scenarios and your solutions to make such integrated audiovisual mining effective.
13.10 General-purpose computers and domain-independent relational database systems have become a large market in the last several decades. However, many people feel that generic data mining systems will not prevail in the data mining market. What do you think? For data mining, should we focus our efforts on developing domain-independent data mining tools or on developing domain-specific data mining solutions? Present your reasoning.
13.11 What is a recommender system? In what ways does it differ from a customer or product-based clustering system? How does it differ from a typical classification or predictive modeling system? Outline one method of collaborative filtering. Discuss why it works and what its limitations are in practice.
13.12 Suppose that your local bank has a data mining system. The bank has been studying your debit card usage patterns. Noticing that you make many transactions at home renovation stores, the bank decides to contact you, offering information regarding their special loans for home improvements.
(a) Discuss how this may conflict with your right to privacy.
(b) Describe another situation in which you feel that data mining can infringe on your privacy.
(c) Describe a privacy-preserving data mining method that may allow the bank to perform customer pattern analysis without infringing on its customers' right to privacy.
(d) What are some examples where data mining could be used to help society? Can you think of ways it could be used that may be detrimental to society?
13.13 What are the major challenges faced in bringing data mining research to market? Illustrate one data mining research issue that, in your view, may have a strong impact on the market and on society. Discuss how to approach such a research issue.
13.14 Based on your view, what is the most challenging research problem in data mining? If you were given a number of years and a good number of researchers and implementors, what would your plan be to make good progress toward an effective solution to such a problem?
13.15 Based on your experience and knowledge, suggest a new frontier in data mining that was not mentioned in this chapter.
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];