Applications of Frequent Pattern Mining - Frequent Pattern Mining - page 453

A number of data benchmarks are also available in order to test the efficiency of
different mining algorithms. Along these, the QUEST synthetic data generator [ 160 ]
is one of the earliest data generators for pattern analysis. This data generator uses an
intuitive model to create transactions as a combination of smaller baskets. Among
real data sets, numerous data sets from the UCI machine learning repository [ 50 ]
have been frequently used for efficiency analysis.

14 Conclusions and Summary

This chapter provides an overview of the key applications of frequent pattern mining.
Frequent pattern mining has a variety of applications to many data mining problems
such as clustering and classification. It also has applications to database problems
such as indexing. Many specific domains such as Web mining and recommendation
analysis, spatiotemporal analysis, multimedia analysis, software bug analysis and
biological analysis can be addressed with frequent pattern mining algorithms. The
main challenge in applying frequent pattern mining to the different domains is that
the constraints and data representations are very different in these domains. Corre-
spondingly, the vanilla frequent pattern mining problem needs to be appropriately
adapted to these domains. It is expected that numerous other applications of frequent
pattern mining algorithms may be found, as new forms of hardware and software
technology create different kinds of data.

References

1. C. C. Aggarwal. On Effective Classification of Strings with Wavelets, ACM KDD Conference ,
2002.
2. C. C. Aggarwal. Managing and Mining Sensor Data, Springer , 2013.
3. C. C. Aggarwal, T. Abdelzaher. Social Sensing, Managing and Mining Sensor Data, Springer ,
2013.
4. C. C. Aggarwal, C. K. Reddy. Data Clustering: Algorithms and Applications, CRC Press ,
2013.
5. C. C. Aggarwal, and H. Wang. Managing and Mining Graph Data, Springer , 2010.
6. C. Aggarwal and P. Yu. On Effective Conceptual Indexing and Similarity Search in Text Data,
ICDM Conference , 2001.
7. C. Aggarwal and C. Zhai. Mining Text Data, Springer , 2012.
8. C. C. Aggarwal, J. Wolf, P. Yu. A New Method for Similarity Indexing of Market Basket Data,
ACM SIGMOD Conference , 1999.
9. C. Aggarwal, C. Procopiuc, and P. Yu. Finding Localized Associations in Market Basket Data,
IEEE Transactions on Knowledge and Data Engineering , 14(1), pp. 51-62, 2002.
10. C. C. Aggarwal, N. Ta, J. Wang, J. Feng, M. Zaki. Xproj: A framework for projected structural
clustering of XML documents, ACM KDD Conference , 2007.
11. R. Agrawal, and R. Srikant. Fast Algorithms for Mining Association Rules in Large Databases,
VLDB Conference , pp. 487-499, 1994.
12. R. Agrawal, and R. Srikant. Mining Sequential Patterns, ICDE Conference , 1995.