Chapter 7 Conclusions and Future Work
7.2 Future Work
With the mining capabilities of the proposed algorithms, there are several interesting extensions on frequent pattern mining and change mining, as listed below.
Resource-aware mining of frequent patterns over data streams.
Resource such as CPU, memory space, and sometimes energy, are very precious in a stream mining environment. They are very likely to be used up when processing data streams which arrive with rapid speed and a huge amount. How to use these resources when we use the proposed algorithms for mining frequent itemsets and changes is an important research issue in our future work.
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Online mining of sequential patterns over data streams with a sliding window.
Online mining of sequential patterns in data streams is more complicated than mining of frequent itemset. There are several challenges of mining of sequential patterns from data streams, such as how to define the meaning of sequential patterns in a stream environment, how to define the model of sliding window for mining sequential patterns of data streams, and how to design an efficient single-pass algorithm for mining the set of sequential patterns from data streams.
Online mining of high utility itemsets over data streams with a sliding window.
Although mining itemsets correlations is important in some applications, in many applications people are more interested in finding out how a set of items that is useful by some measure, such as utility. The frequent itemsets do not reflect the impact of any other factor except frequency of the presence or absence of an item. Frequent itemsets may only contribute a small portion of the overall profit, whereas infrequent itemsets may contribute a large portion of the profit. Hence, utility mining is likely to be useful in a wide range of practical application. There are several challenges on mining high utility itemsets over data streams, such as how to define the model of sliding window for mining high utility itemsets of data streams, how to define the meaning of high utility itemsets in a stream environment, and how to design an efficient one-pass algorithm for discovering the set of high utility itemsets from data streams with a sliding window.
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Publication List
Journal Papers
1. Hua-Fu Li, Suh-Yin Lee, and Man-Kwan Shan (2006), "DSM-PLW: Single-Pass Mining of Path Traversal Patterns over Streaming Web Click-Sequences," Computer Networks: Special Issue on Web Dynamics, Volume 50, Issue 10, July 2006, pp.
1474-1487. (SCI-E, JCR 2004 IF = 1.226)
2. Hua-Fu Li, Suh-Yin Lee, and Man-Kwan Shan (2005), "Online Mining Changes of Items over Continuous Append-only and Dynamic Data Streams," Journal of Universal Computer Science: Special Issue on Knowledge Discovery in Data Streams, Volume 11, No. 8, 2005, pp. 1411-1425. (SCI-E, JCR 2004 IF=0.456) 3. Hua-Fu Li, Suh-Yin Lee, and Man-Kwan Shan (2005), "Online Mining Maximal
Frequent Structures in Continuous Landmark Melody Streams," Pattern Recognition Letters, Volume 26, Issue 11, August 2005, pp. 1658-1674 (SCI-E, JCR 2004 IF=0.576)
4. Hua-Fu Li, Man-Kwan Shan, and Suh-Yin Lee (2006), “DSM-FI: An Efficient Algorithm for Mining Frequent Itemsets in Data Streams, “Knowledge and Information Systems: An International Journal, under revision. (SCI-E & EI)
5. Hua-Fu Li, Man-Kwan Shan, and Suh-Yin Lee (2006), “DSM-TKP: Mining Top-K Path Traversal Patterns over Web Click-Streams,” in preparation.
6. Hua-Fu Li, Man-Kwan Shan, and Suh-Yin Lee (2006), “Efficient Maintenance and Mining of Frequent Itemsets over Stream Sliding Windows, “in preparation.
7. Hua-Fu Li, Man-Kwan Shan, and Suh-Yin Lee, Online Mining of Frequent Query Trees over Data Streams, in preparation.
8. Hua-Fu Li, Man-Kwan Shan, and Suh-Yin Lee, Mining and Detecting Changes in
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User-Centered Music Query Streams, in preparation.
Conference Papers
1. Hua-Fu Li, Chin-Chuan Ho, Man-Kwan Shan, and Suh-Yin Lee, " Efficient Maintenance and Mining of Frequent Itemsets over Stream Sliding Windows," in Proc. of IEEE International Conference on Systems, Man, and Cybernetic (IEEE SMC-2006), Taipei, Taiwan, October 8-10, 2006. (EI)
2. Hua-Fu Li, Man-Kwan Shan, and Suh-Yin Lee, "Detecting Changes in User-Centered Music Query Streams," in Proc. of IEEE International Conference on Multimedia and Expo (ICME-2006), Toronto, Ontario, Canada, July 9-12, 2006.
(EI)
3. Hua-Fu Li, Chin-Chuan Ho, Man-Kwan Shan, and Suh-Yin Lee, "Online Mining of Recent Music Query Streams," in Proc. of IEEE International Conference on Multimedia and Expo (ICME-2006), Toronto, Ontario, Canada, July 9-12, 2006.
(EI)
4. Hua-Fu Li, Man-Kwan Shan, and Suh-Yin Lee, "Online Mining of Frequent Query Trees over Data Streams," in Proc. of the 15th World Wide Web Conference (WWW-2006), Edinburgh, Scotland, May 23-26, 2006. (EI) (poster)
5. Hua-Fu Li, Suh-Yin Lee, and Man-Kwan Shan, "DSM-TKP: Mining Top-K Path Traversal Patterns over Web Click-Streams," in Proc. of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence (WI 2005), France, September 19-22, 2005. (EI)
6. Hua-Fu Li, Suh-Yin Lee, and Man-Kwan Shan, "Online Mining (Recently) Maximal Frequent Itemsets over Data Streams," in Proc. of the 15th IEEE International Workshop on Research Issues on Data Engineering (RIDE2005),
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Tokyo, Japan, April 3-4, 2005. (EI)
7. Hua-Fu Li, Suh-Yin Lee, and Man-Kwan Shan, "Mining Maximal Frequent Itmesets in Data Streams," in Proc. of 2004 International Computer Symposium (ICS2004), Taipei, Taiwan, December 15-17, 2004. (Best Paper Award)
8. Hua-Fu Li, Suh-Yin Lee, and Man-Kwan Shan, "An Efficient Algorithm for Mining Frequent Itemsets over the Entire History of Data Streams," in the Proc. of First International Workshop on Knowledge Discovery in Data Streams, to be held in conjunction with the 15th European Conference on Machine Learning (ECML 2004) and the 8th European Conference on the Principals and Practice of Knowledge Discovery in Databases (PKDD 2004), Pisa, Italy, September 20-24, 2004.
9. Hua-Fu Li, Suh-Yin Lee, and Man-Kwan Shan, "On Mining Webclick Streams for Path Traversal Patterns," in Proc. of the 13th World Wide Web Conference (WWW2004), New York, May 17-22, 2004. (EI)
10. Hua-Fu Li, Suh-Yin Lee, and Man-Kwan Shan, "Mining Frequent Closed Structures in Streaming Melody Sequences," in Proc. of IEEE International Conference on Multimedia and Expo (ICME 2004), Taipei, Taiwan, 2004.(EI) 11. Hua-Fu Li and Suh-Yin Lee, "Single-Pass Algorithms for Mining Frequency
Change Patterns with Limited Space in Evolving Append-only and Dynamic Transaction Data Streams", in the Proc. of the 2004 IEEE International Conference on e-Technology, e-Commerce and e-Service (EEE-04), Taipei, Taiwan, 2004.
12. Man-Kwan Shan and Hua-Fu Li, "Fast Discovery of Structure Navigation Patterns from Web User Traversals," in Proc. of SPIE Conference on Data Mining and Knowledge Discovery: Theory, Tools, and Technology IV, Orlando, Florida, USA, 2002. (EI)
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13. Hua-Fu Li and Man-Kwan Shan, "PNP: Mining of Profile Navigational Patterns,"
in Proc. of SPIE Conference on Data Mining and Knowledge Discovery: Theory, Tools, and Technology IV, Orlando, Florida, USA, 2002. (EI)
14. Hua-Fu Li and Man-Kwan Shan, "Mining Non-Simple Traversal Paths from Web Access Logs," in Proc. of 2000 Workshop on Internet and Distributed Systems, Tainan, Taiwan, 2000.
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Vita
Hua-Fu Li (李華富) was born on February 24, 1976 in Taoyuan, Taiwan, Republic of China. He received the BS degree in Computer Science and Engineering from Tatung Institute of Technology and the MS degree in Computer Science from National Chengchi University, in 1998 and 2000, respectively. He is currently working towards the Ph.D. degree in National Chiao-Tung University. He coauthored with his advisor Dr. Suh-Yin Lee for their works which received the 2004 ICS (International Computer Symposium) Best Paper Award.
His research interests include data mining, data stream management, multimedia information systems and bioinformatics.