Abstract
High average-utility pattern mining is an emerging issue in the association rule mining area due to its meaningful mining results reflecting the characteristics of items such as their importance and quantities, and consideration on lengths of patterns. Recently, various studies have been dedicated to the researches of mining methods that extract up-to-date patterns from stream data, which are continually generated from various sources without limitations. A sliding window technique is one of methods for handling such stream data and mining up-to-date patterns. In this paper, we introduce state-of-the-art algorithms for finding up-to-date high average-utility patterns over data stream by using the sliding window method.
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Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: 20th International Conference on Very Large Data Bases, pp. 487–499 (1994)
Chen, L., Mei, Q.: Mining frequent items in data stream using time fading model. Inf. Sci. 257, 54–69 (2014)
Han, J., Pei, J., Yin, Y., Mao, R.: Mining frequent patterns without candidate generation: a frequent-pattern tree approach. Data Mining Knowl. Discov. 8(1), 53–87 (2004)
Hong, T.-P., Lee, C.-H., Wang, S.-L.: Effective utility mining with the measure of average utility. Expert Syst. Appl. 38, 8259–8265 (2011)
Li, H., Shan, M., Lee, S.: DSM-FI: an efficient algorithm for mining frequent itemsets in data streams. Knowl. Inf. Syst. 17(1), 1151–1163 (2008)
Ryang, H., Yun, U.: High utility pattern mining over data streams with sliding window technique. Expert Syst. Appl. 57(15), 214–231 (2016)
Yun, U., Ryang, H.: Incremental high utility pattern mining with static and dynamic databases. Appl. Intell. 42(2), 323–352 (2015)
Yun, U., Kim, D., Ryang, H., Lee, G., Lee, K.: Mining recent high average utility patterns based on sliding window from stream data. J. Intell. Fuzzy Syst. 30, 3605–3617 (2016)
Acknowledgments
This research was supported by the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (NRF No. 20152062051 and NRF No. 20155054624).
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Kim, D., Yun, U. (2017). State-of-the-Art Algorithms for Mining Up-to-Date High Average-Utility Patterns. In: Park, J., Pan, Y., Yi, G., Loia, V. (eds) Advances in Computer Science and Ubiquitous Computing. UCAWSN CUTE CSA 2016 2016 2016. Lecture Notes in Electrical Engineering, vol 421. Springer, Singapore. https://doi.org/10.1007/978-981-10-3023-9_18
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DOI: https://doi.org/10.1007/978-981-10-3023-9_18
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