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. Bolaños, 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.
   


© 1982-2017 Mehran University Research Journal Of Engineering & Technology, Jamshoro, 76062, Sindh, Pakistan. All Rights Reserved.