Data Mining_ Concepts and Techniques - Jiawei Han [378]
■ A major social concern of data mining is the issue of privacy and data security. Privacy-preserving data mining deals with obtaining valid data mining results without disclosing underlying sensitive values. Its goal is to ensure privacy protection and security while preserving the overall quality of data mining results.
■ Data mining trends include further efforts toward the exploration of new application areas; improved scalable, interactive, and constraint-based mining methods; the integration of data mining with web service, database, warehousing, and cloud computing systems; and mining social and information networks. Other trends include the mining of spatiotemporal and cyber-physical system data, biological data, software/system engineering data, and multimedia and text data, in addition to web mining, distributed and real-time data stream mining, visual and audio mining, and privacy and security in data mining.
13.7. Exercises
13.1 Sequence data are ubiquitous and have diverse applications. This chapter presented a general overview of sequential pattern mining, sequence classification, sequence similarity search, trend analysis, biological sequence alignment, and modeling. However, we have not covered sequence clustering. Present an overview of methods for sequence clustering.
13.2 This chapter presented an overview of sequence pattern mining and graph pattern mining methods. Mining tree patterns and partial order patterns is also studied in research. Summarize the methods for mining structured patterns, including sequences, trees, graphs, and partial order relationships. Examine what kinds of structural pattern mining have not been covered in research. Propose applications that can be created for such new mining problems.
13.3 Many studies analyze homogeneous information networks (e.g., social networks consisting of friends linked with friends). However, many other applications involve heterogeneous information networks (i.e., networks linking multiple types of object such as research papers, conference, authors, and topics). What are the major differences between methodologies for mining heterogeneous information networks and methods for their homogeneous counterparts?
13.4 Research and describe a data mining application that was not presented in this chapter. Discuss how different forms of data mining can be used in the application.
13.5 Why is the establishment of theoretical foundations important for data mining? Name and describe the main theoretical foundations that have been proposed for data mining. Comment on how they each satisfy (or fail to satisfy) the requirements of an ideal theoretical framework for data mining.
13.6 (Research project) Building a theory of data mining requires setting up a theoretical framework so that the major data mining functions can be explained under this framework. Take one theory as an example (e.g., data compression theory) and examine how the major data mining functions fit into this framework. If some functions do not fit well into the current theoretical framework, can you propose a way to extend the framework to explain these functions?
13.7 There is a strong linkage between statistical data analysis and data mining. Some people think of data mining as automated and scalable methods for statistical data analysis. Do you agree or disagree with this perception? Present one statistical analysis method that can be automated and/or scaled up nicely by integration with current data mining methodology.
13.8 What are the differences between visual data mining and data visualization? Data visualization may suffer from the data abundance problem. For example, it is not easy to visually discover interesting properties of network connections if a social network is huge,