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Erschienen in:
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2021 | OriginalPaper | Buchkapitel

Analytics of Multiple-Threshold Model for High Average-Utilization Patterns in Smart City Environments

verfasst von : Jerry Chun-Wei Lin, Ting Li, Philippe Fournier-Viger, Ji Zhang

Erschienen in: Data-Driven Mining, Learning and Analytics for Secured Smart Cities

Verlag: Springer International Publishing

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Abstract

For the accelerated development in ICT technology and computers, data mining and pattern analytics are used to reveal potential patterns for decision-making in smart city environments. Past works of pattern mining in smart cities focused on frequency constraint that cannot show the patterns involved with multi-factors for evaluation. Also, single-threshold value is mostly considered in the pattern-mining framework, which is not realistic in smart city environments since different infrastructures should have different tolerance factors for pattern analytics. In this paper, we then employ the multi-threshold constraint to evaluate the high utilization patterns that can be applied in the smart city environments. The average-utilization model is also adapted in the designed model that provides a fair and alternative criterion for pattern analytics. Based on the provided results in the experiments, the designed framework shows better effectiveness and efficiency in pattern mining task that can be deployed to analyze the utilization of the varied infrastructure in smart city environments.

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Nächstes Kapitel Artificial Intelligence and Machine Learning for Ensuring Security in Smart Cities
Fußnoten
Literatur
1.
Agarwal R, Imielinski T, Swami A (1993) Database mining: a performance perspective. IEEE Trans Knowl Data Eng 5(6):914–925CrossRef
2.
Han J, Pei J, Yin Y, Mao R (2004) Mining frequent patterns without candidate generation: a frequent-pattern tree approach. Data Min Knowl Disc 8(1):53–87MathSciNetCrossRef
3.
Agrawal R, Srikant R (1994) Fast algorithms for mining association rules in large databases. In: International conference on very large data bases, pp 487–499
4.
Chen MS, Han J, Yu PS (1996) Data mining: an overview from database perspective. IEEE Trans Knowl Data Eng 8(6):866–883CrossRef
5.
Chan R, Yang Q, Shen YD (2003) Mining high utility itemsets. In: IEEE international conference on data mining, pp 19–26
6.
Liu Y, Liao WK, Choudhary A (2005) A two-phase algorithm for fast discovery of high utility itemsets. Lect Notes Comput Sci 3518:689–695CrossRef
7.
Yao H, Hamilton HJ, Butz CJ (2004) A foundational approach to mining itemset utilities from databases. In: SIAM international conference on data mining, pp 211–225
8.
Yun U, Ryang H, Ryu KH (2014) High utility itemset mining with techniques for reducing overestimated utilities and pruning candidates. Expert Syst Appl 41(8):3861–4387CrossRef
9.
Hong TP, Lee CH, Wang SL (2011) Effective utility mining with the measure of average utility. Expert Syst Appl 38(7):8259–8265CrossRef
10.
Lin CW, Hong TP, Lu WH (2010) Effeciently mining high average utility itemsets with a tree structure. Lect Notes Comput Sci 5990:131–139CrossRef
11.
Lan GC, Hong TP, Tseng VS (2012) A projection-based approach for discovering high average-utility itemsets. J Inf Sci Eng 28(1):193–209
12.
Lu T, Vo B, Nguyen HT, Hong TP (2014) A new method for mining high average utility itemsets. Comput Inf Syst Ind Manag 8838:33–42
13.
Tseng VS, Wu CW, Shie BE, Yu PS (2010) UP-growth: an efficient algorithm for high utility itemset mining. In: ACM SIGKDD international conference on knowledge discovery and data mining, pp 253–262
14.
Tseng VS, Shie BE, Wu CW, Yu PS (2013) Efficient algorithms for mining high utility itemsets from transactional databases. IEEE Trans Knowl Data Eng 25(8):1772–1786CrossRef
15.
Liu M, Qu J (2012) Mining high utility itemsets without candidate generation. In: ACM international conference on information and knowledge management, pp 55–64
16.
Fournier-Viger P, Wu CW, Zida S, Tseng VS (2014) FHM: faster high-utility itemset mining using estimated utility co-occurrence pruning. Lect Notes Comput Sci 8502:83–92CrossRef
17.
Krishnamoorthy S (2015) Pruning strategies for mining high utility itemsets. Expert Syst Appl 42(5):2371–2381CrossRef
18.
Lin JCW, Gan W, Hong TP, Tseng VS (2015) Efficient algorithms for miningup-to-date high-utility patterns. Adv Eng Inform 29(3):648–661CrossRef
19.
Tseng VS, Wu CW, Fournier-Viger P, Yu PS (2016) Efficient algorithms for mining top-K high utility itemsets. IEEE Trans Knowl Data Eng 208(1):54–67CrossRef
20.
Liu, B., Hsu, W., and Ma, Y.: Mining association rules with multiple minimum support. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining pp. 337–341 (1999)
21.
Kiran RU, Reddy PK (2011) Novel techniques to reduce search space in multiple minimum supports-based frequent pattern mining algorithms. In: ACM international conference on extending database technology, pp 11–20
22.
Ryang H, Yun U, Ryu K (2014) Discovering high utility itemsets with multiple minimum supports. Intell Data Anal 18(6):1027–1047CrossRef
23.
Lin JCW, Gan W, Fournier-Viger P, Hong TP, Zhan J (2018) Efficient mining of high-utility itemsets using multiple minimum utility thresholds. Knowl-Based Syst 69:112–126
24.
Fournier-Viger P, Lin JCW, Gomariz A, Gueniche T, Soltani A, Deng Z, Lam HT (2016) The SPMF open-source data mining library version 2. In: Joint European conference on machine learning and knowledge discovery in databases, pp 36–40
25.
Agrawal R, Srikant R (2004) Quest synthetic data generator. Data Min Knowl Disc 8(1):53–87CrossRef
Metadaten
Titel
Analytics of Multiple-Threshold Model for High Average-Utilization Patterns in Smart City Environments
verfasst von
Jerry Chun-Wei Lin
Ting Li
Philippe Fournier-Viger
Ji Zhang
Copyright-Jahr
2021
Buch
Data-Driven Mining, Learning and Analytics for Secured Smart Cities

Print ISBN: 978-3-030-72138-1

Electronic ISBN: 978-3-030-72139-8

Copyright-Jahr: 2021

DOI
https://doi.org/10.1007/978-3-030-72139-8_1

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