An Efficient High Utility Itemset Mining Approach using Predicted Utility Co-exist Pruning
Keywords:
Data Mining, Frequent Itemset, High Utility itemset, Utility List, Transaction Weighted UtilityAbstract
The traditional frequent item-set mining is most popular and widely used technique for mining of related items. It considers whether the item is present or absence in dataset. However, item quantity and its importance is need to be consider for some real-world problem such as identify profitable items from the customer transaction dataset in supermarket, discover valuable customer for business, in medical field identify the combination of symptoms that are more significant to diseases. High utility itemset mining considers item quantity and its importance. Many researches have been done on the high utility itemset mining. Among them, utility list-based methods are efficient as it does not generate the candidate set. However, drawback of such techniques is lot of expensive join operations on utility list which degrades the performance of algorithm by increasing the storage requirement and time for execution. We proposed Predicted Utility Co-Exist Structure known as PUCS to store the utility data and Predicted Utility Co-Exist Pruning known as PUCP to eliminate unnecessary utility list join operations. It improves the algorithm’s performance. We experiment the proposed approach on standard real-life datasets and results are compared with existing methods. According to experimental result analysis, proposed PUCP-miner outperforms existing approaches concerning execution time and memory requirement. In terms of execution time, proposed approach achieves more than 20 % improvement and for memory consideration, proposed approach got 3% improvement compared to state of the art approaches.
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References
D.-N. Le Ashour, Amira S., Nilanjan Dey, "Biological data mining: Techniques and applications,"Min. Multimed. Doc., vol. 1, no. 4, pp. 161–172, 2017.
R. S. Agrawal, Rakesh, "Fast algorithms for mining association rules,"Proc. 20th int. conf. very large data bases, VLDB, vol. 1215, pp. 487–499, 1994.
H. J. H. Yao, Hong, "Mining itemset utilities from transaction databases,"Data Knowl. Eng. 59, vol. 59, no. 3, pp. 603–626, 2006.
Malla, S., M. J. . Meena, O. . Reddy. R, V. . Mahalakshmi, and A. . Balobaid. “A Study on Fish Classification Techniques Using Convolutional Neural Networks on Highly Challenged Underwater Images”. International Journal on Recent and Innovation Trends in Computing and Communication, vol. 10, no. 4, Apr. 2022, pp. 01-09, doi:10.17762/ijritcc.v10i4.5524.
A. M. Hu, Jianying, "High-utility pattern mining : A method for discovery of high-utility item sets,"Pattern Recognit., vol. 40, no. 11, pp. 3317–3324, 2007.
Y. Liu, W. Liao, and A. Choudhary, "A two-phase algorithm for fast discovery of high utility itemsets,"Pacific-Asia Conf. Knowl. Discov. Data Mining, Springer, Berlin, Heidelb., pp. 689–695, 2005.
J. Han, J. Pei, and Y. Yin, "Mining FrequentPatterns without Candidate Generation,"ACM sigmod Rec., vol. 1, no. 29, pp. 1–12, 2000.
Kose, O., & Oktay, T. (2022). Hexarotor Yaw Flight Control with SPSA, PID Algorithm and Morphing. International Journal of Intelligent Systems and Applications in Engineering, 10(2), 216–221. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/1879
Ho R. Ryang, Heungmo, Unil Yun, "Fast algorithm for high utility pattern mining with the sum of item quantities,"Intell. Data Anal., vol. 20, no. 2, pp. 395–415, 2016.
V. Tseng, C. Wu, B. Shie, and P. Yu, "UP-Growth: an efficient algorithm for high utility itemset mining,"Discov. Data Min., pp. 253–262, 2010.
Y. Shen, "Objective-Oriented Utility-Based Association Mining," in In 2002 IEEE International Conference on Data Mining, 2002. Proceedings, 2002, pp. 426–433.
P. F.-V. Qu, Jun-Feng, Mengchi Liu, "Efficient Algorithms for High Utility Itemset Mining without Candidate Generation,"High-Utility Pattern Mining, Springer, Cham, 2019.
Sally Fouad Shady. (2021). Approaches to Teaching a Biomaterials Laboratory Course Online. Journal of Online Engineering Education, 12(1), 01–05. Retrieved from http://onlineengineeringeducation.com/index.php/joee/article/view/43
V. S. Tseng, B. Shie, C. Wu, and P. S. Yu, "Efficient Algorithms for Mining High Utility Itemsets from Transactional Databases,"IEEE Trans. Knowl. Data Eng., vol. 25, no. 8, pp. 1772–1786, 2012.
W. Song, Y. Liu, and J. Li, "Mining high utility itemsets by dynamically pruning the tree structure,"Springer Sci. Media New York, pp. 29–43, 2014.
Gill, D. R. . (2022). A Study of Framework of Behavioural Driven Development: Methodologies, Advantages, and Challenges. International Journal on Future Revolution in Computer Science &Amp; Communication Engineering, 8(2), 09–12. https://doi.org/10.17762/ijfrcsce.v8i2.2068
A. Y. Peng, Y. S. K. B, and P. Riddle, "mHUIMiner : A Fast High Utility Itemset Mining Algorithm for Sparse Datasets,"Pacific-Asia Conf. Knowl. Discov. Data Mining, Springer, Cham, pp. 196–207, 2017.
J. Liu, M Qu, "Mining High Utility Itemsets without Candidate Generation Categories and Subject Descriptors," in Proceedings of the 21st ACM international conference on Information and knowledge management, 2012, pp. 55–64.
H. Yao, H. J. Hamilton, and C. J. Butz, "A Foundational Approach to Mining Itemset Utilities from Databases,"Proc. 2004 SIAM Int. Conf. Data Min., vol. Society fo, pp. 482–486, 2004.
Li, Yu-Chiang, Jieh-Shan Yeh, "Isolated items discarding strategy for discovering high utility itemsets,"Data Knowl. Eng., vol. 64, no. 1, pp. 198–217, 2008.
Q. D. Philippe, F. H. Ramampiaro, and K. Nørv, "Efficient high utility itemset mining using buffered utility-lists,"Appl. Intell., vol. 48, no. 7, pp. 1859–1877, 2018.
P. Fournier-Viger, C. W. Wu, S. Zida, and V. S. Tseng, "FHM: Faster high-utility itemset mining using estimated utility co-occurrence pruning,"Springer, Cham. pp. 83–92, 2014.
V. Tseng, C. Wu, B. Shie, and P. Yu, "UP-Growth: an efficient algorithm for high utility itemset mining," in Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, 2010, pp. 253–262.
M. Liu and J. Qu, "Mining high utility itemsets without candidate generation,"ACM Int. Conf. Proceeding Ser., pp. 55–64, 2012.
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