Abstract
Many users in social web environments share and publish user-generated contents such as tastes, opinions, and ideas in the form of text and multimedia data. Various research studies have been conducted on the analysis of such social data, which can be used for discovering users’ thoughts on specific topics. But, there are still challenging tasks to find out the meaningful patterns from the social data due to rapidly increasing amount of data. In this paper, we therefore propose a rule-based topic trend analysis by using On-Line-Analytical Processing (OLAP) and Association Rule Mining (ARM) to detect information such as previously unknown or abnormal events or situations. For the verification of the proposed method, we conduct experiments to demonstrate that the method is feasible to perform rule-based topic trend analysis.
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References
Chaudhuri, S., Dayal, U.: An overview of data warehousing and OLAP technology. ACM Sigmod Rec. 26(1), 65–74 (1997)
Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. ACM SIGMOD Rec. 22(2), 207–216 (1993)
Park, D., et al.: NetCube: a comprehensive network traffic analysis model based on multidimensional OLAP data cube. Int. J. Netw. Manag. 23(2), 101–118 (2013)
Sohn, J.-S., Chung, I.-J.: Dynamic FOAF management method for social networks in the social web environment. J. Supercomput. 66(2), 633–648 (2013)
Han, J., Kamber, M., Pei, J.: Data Mining: Concepts and Techniques (2011). Elsevier
Sarwar, B., et al.: Analysis of recommendation algorithms for e-commerce. In: Proceedings of the 2nd ACM Conference on Electronic Commerce. ACM (2000)
Inokuchi, A., Washio, T., Motoda, H.: An apriori-based algorithm for mining frequent substructures from graph data. In: Principles of Data Mining and Knowledge Discovery, pp. 13–23. Springer, Heidelberg (2000)
Lim, A.H., Lee, C.-S., Raman, M.: Hybrid genetic algorithm and association rules for mining workflow best practices. Expert Syst. Appl. 39(12), 10544–10551 (2012)
Lee, C., et al.: A hybrid OLAP-association rule mining based quality management system for extracting defect patterns in the garment industry. Expert Syst. Appl. 40(7), 2435–2446 (2013)
Ma, B., et al.: Semantic search for public opinions on urban affairs: a probabilistic topic modeling-based approach. Inf. Process. Manag. 52(3), 430–445 (2015)
Acknowledgment
This research was supported by the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning (grand number NRF-2016R1A2B1010975).
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Jeon, Y., Cho, C., Seo, J., Kwon, K., Park, H., Chung, IJ. (2017). Rule-Based Topic Trend Analysis by Using Data Mining Techniques. In: Park, J., Chen, SC., Raymond Choo, KK. (eds) Advanced Multimedia and Ubiquitous Engineering. FutureTech MUE 2017 2017. Lecture Notes in Electrical Engineering, vol 448. Springer, Singapore. https://doi.org/10.1007/978-981-10-5041-1_75
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DOI: https://doi.org/10.1007/978-981-10-5041-1_75
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