Mehran University Research Journal Of Engineering &
Technology (HEC Recognized In Category "X")
Publishing Since 1982.



For Authors
For Readers
Article Information  
Mining Frequent Item Sets in Asynchronous Transactional Data Streams over Time Sensitive Sliding Windows Model

Keywords: Data Mining, Data Stream, Frequent Pattern, Transactional Data, Sliding Windows.

Mehran University Research Journal of Engineering & Technology

Volume 35 ,  Issue 4

QAISAR   JAVAID , FARIDA MEMON   , SHAHNAWAZ TALPUR   , MUHAMMAD ARIF   , MUHAMMAD DAUD AWAN   ,

References
1. Lee, G., Yun, U., and Ryu, K.H., "Sliding Window Based Weighted Maximal Frequent Pattern Mining Over Data Streams", Expert Systems with Applications, Volume 41, pp. 694-708, 2014.
2. Jin, R., Abu-Ata, M., Xiang, Y., and Ruan, N, "Effective and Efficient Itemset Pattern Summarization: Regression-Based Approaches", Proceedings of 14th ACM International Conference on Knowledge Discovery and Data Mining, pp. 399-407, 2008.
3. Faisal, M. A., Aung, Z., Williams, J.R., and Sanchez, A., "Data-Stream-Based Intrusion Detection System for Advanced Metering Infrastructure in Smart Grid: A Feasibility Study", IEEE Systems Journal, Volume 9, No. 1, pp. 1-14, 2014.
4. Pyun, G., Yun, U., and Ryu, K.H., "Efficient Frequent Pattern Mining Based on Linear Prefix Tree", Knowledge-Based Systems, Volume 55, pp. 125-139, 2014.
5. Tao, F., Murtagh, F., and Farid, M., "Weighted Association Rule Mining using Weighted Support and Significance Framework", Proceedings of 9th ACM International Conference on Knowledge Discovery and Data Mining, pp. 661-666, 2003.
6. Liu, G., Lu, H., Xu, Y., and Yu, J.X., "Ascending Frequency Ordered Prefix-Tree: Efficient Mining of Frequent Patterns", Proceedings of 8th International Conference on Database Systems for Advanced Applications, pp. 65-72, 2003
7. Borgelt, C., "An Implementation of the FP-Growth Algorithm", Proceedings of 1st International Workshop on Open Source Data Mining: Frequent Pattern Mining Implementations, pp. 1-5, 2005
8. Chen, L., and Mei, Q., "Mining Frequent Items in Data Stream using Time Fading Model", Information Sciences, Volume 257, pp. 54-69, 2014.
9. BolaƱos, M., Forrest, J., and Hahsler, M., "Clustering Large Datasets using Data Stream Clustering Techniques", Data Analysis, Machine Learning and Knowledge Discovery, Springer, pp. 135-143, 2014.
10. Sun, Z., Mao, K., Tang, W., Mak, L.-O., Xian, K., and Liu, Y., "Knowledge-Based Evolving Clustering Algorithm for Data Stream", 11th International Conference on Service Systems and Service Management, pp. 1-6, 2014.
11. Chakraborty, S.B., and Shaikh, M., "A Comprehensive and Relative Study of Detecting Deformed Identity Crime with Different Classifier Algorithms and Multilayer Mining Algorithm", Analysis, Volume 3, 2014.
12. Shie, B.-E., Yu, P.S., and Tseng, V.S., "Efficient Algorithms for Mining Maximal High Utility Item sets from Data Streams with Different Models", Expert Systems with Applications, Volume 39, pp. 12947-12960, 2012
13. Yun, U., Shin, H., Ryu, K.H., and Yoon, E., "An Efficient Mining Algorithm for Maximal Weighted Frequent Patterns in Transactional Databases", Knowledge-Based Systems, Volume 33, pp. 53-64, 2012
14. Feng, J., Yan, Z., Kang, Y., Wang, J., and An, L., "MFISW:A New Method for Mining Frequent Item sets in Time and Transaction Sensitive Sliding Window", 6th International Conference on Fuzzy Systems and Knowledge Discovery, pp. 270-274, 2009.
15. Ahmed, C.F., Tanbeer, S.K., Jeong, B.-S., and Lee, Y.-K, "Efficient Tree Structures for High Utility Pattern Mining in Incremental Databases", IEEE Transactions on Knowledge and Data Engineering, Volume 21, pp. 1708-1721, 2009.
16. Ye, Y., and Chiang, C.-C., "A Parallel Apriori Algorithm for Frequent Itemsets Mining", 4th International Conference on Software Engineering Research, Management and Applications, pp. 87-94, 2006.
17. Dang, X.H., Ong, K.-L., and Lee, V., "An Adaptive Algorithm for Finding Frequent Sets in Landmark Windows", Scalable Uncertainty Management, Springer Verlag Berlin Heidelberg, pp. 590-597, 2012.
18. Deypir, M., and Sadreddini, M.H, "A Dynamic Layout of Sliding Window for Frequent Itemset Mining Over Data Streams", Journal of Systems and Software, Volume 85, pp. 746-759, 2012.
19. Jea, K.-F., Li, C.-W., Hsu, C.-W., Lin, R.-P., and Yen, S.- F., "A Load Shedding Scheme for Frequent Pattern Mining in Transactional Data Streams", 8th International Conference on Fuzzy Systems and Knowledge Discovery, pp. 1294-1299, 2011.
20. Thanh, L.H., and Calders, T., "Mining Top-k Frequent Items in a Data Stream with Flexible Sliding Windows", Proceedings of 16th ACM International Conference on Knowledge Discovery and Data Mining, pp. 283-292, 2010.
21. Deypir, M., Sadreddini, M.H., and Hashemi, S., "Towards a Variable Size Sliding Window Model for Frequent Itemset Mining Over Data Streams", Computers and Industrial Engineering, Volume 63, pp. 161-172, 2012.
22. Rashid, M.M., Karim, M.R., Jeong, B.-S., and Choi, H.- J., "Efficient Mining Regularly Frequent Patterns in Transactional Databases", Database Systems for Advanced Applications, Volume 7238, pp. 258-271, 2012
23. Wang, X., Yue, K., Niu, W., and Shi, Z., "An Approach for Adaptive Associative Classification", Expert Systems with Applications, Volume 38, pp. 11873-11883, 2011.
24. Cesario, E., Grillo, A., Mastroianni, C., and Talia, D., "A Sketch-Based Architecture for Mining Frequent Items and Itemsets from Distributed Data Streams", 11th IEEE/ ACM International Symposium on Cluster, Cloud and Grid Computing, pp. 245-253, 2011.
25. Parmar, M.A., Sutaria, M.K., and Joshi, M.K., "An Approach for Finding Frequent Item Set Done By Comparison Based Technique", International Journal of Computer Science and Mobile Computing, Volume 3, pp. 996-1001, 2014.
26. Zhang, L., Wang, M., Gu, Q., Zhai, Z., and Wang, G., "Efficient Mining Frequent Closed Resource Patterns in Resource Effectiveness Data: The MFPattern Approach", Proceedings of 1st Symposium on Aviation Maintenance and Management, Volume 2, pp. 31-41, 2014.
27. Dhull, A., and Yadav, N., "Mining Maximum Frequent Item Sets Over Data Streams Using Transaction Sliding Window Techniques", International Journal of Computer Science and Network Security, Volume 14, No. 2, pp. 85-85, 2014.
28. Zheng, Z., Kohavi, R., and Mason, L., "Real World Performance of Association Rule Algorithms", Proceedings of 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 401-406, 2001.
29. Gouider, M.S., and Zarrouk, M., "Frequent Patterns Mining in Time-Sensitive Data Stream", International Journal of Computer Science Issues, Volume 9, Issue 4, No. 2, pp. 117-124, 2012.