![]() ![]() Given that the value of trade in 2017 was $114 billion and in 2018 was $160 billion, the 2020 goal appears feasible. ![]() Both Korea and ASEAN agreed to make efforts to reach $200 billion in trade by 2020. Second, the goal of a “prosperity community” was announced to build a cornerstone for mutually beneficial, future-oriented economic cooperation. 2.46 million ASEAN visitors came to Korea, and around 9 million Koreans visited ASEAN countries in 2018. First, the goal of a “people community” was set to reach “15 millions of mutual visitors by 2020 annually” through cultural exchanges and supporting human resource capacity building projects. To date, a total of 57 key projects have been implemented following the addition of seven projects. At the 2nd plenary meeting in April 2019, 50 key projects under 16 initiatives were determined. The Presidential Committee on New Southern Policy, which was established in August 2018, announced promotion strategies in November 2018 after its 1st plenary meeting. The NSP was first announced in November 2017 in Indonesia by President Moon, proclaiming a vision of a “people-centered community of peace and prosperity” with three communities centered on people, prosperity, and peace. It is a novel attempt to put ASEAN member states (AMS) and India at the center of Korea’s foreign policy agenda. e-NSP is tens to thousands of times faster than baseline approaches, and offers a sound and effective approach for efficient mining of NSP in large scale datasets by directly using existing PSP mining algorithms.The New Southern Policy (NSP) aims to elevate Korea’s strategic ties with the Association of Southeast Asian Nations (ASEAN), including its member states, and with India, in the process cooperating with Korea’s four major diplomatic partners: The United States, China, Japan, and Russia. e-NSP is compared with two currently available NSP mining algorithms via intensive experiments on three synthetic and six real-life datasets from aspects including data characteristics, computational costs and scalability. Theoretical analyses show that e-NSP performs particularly well on datasets with a small number of elements in a sequence, a large number of itemsets and low minimum support. Finally, a simple but efficient strategy is proposed to generate NSC. This not only avoids the need for additional database scans, but also enables the use of existing PSP mining algorithms to mine for NSP. The NSC supports are then calculated based only on the corresponding PSP. Second, an efficient approach is proposed to convert the negative containment problem to a positive containment problem. Accordingly, negative containment is first defined to determine whether a data sequence contains a negative sequence based on set theory. This paper proposes a very innovative and efficient theoretical framework: set theory-based NSP mining (ST-NSP), and a corresponding algorithm, e-NSP, to efficiently identify NSP by involving only the identified PSP, without re-scanning the database. This has been shown to be very inefficient or even impractical, since the NSC search space is usually huge. So far, the problem has not been formalized well, and very few approaches have been proposed to mine for specific types of NSP, which rely on database re-scans after identifying PSP in order to calculate the NSC supports. However, discovering NSP is much more difficult than identifying PSP due to the significant problem complexity caused by non-occurring elements, high computational cost and huge search space in calculating negative sequential candidates (NSC). ![]() ![]() using a medical service) in many intelligent systems and applications such as intelligent transport systems, healthcare and risk management, as they often involve non-occurring but interesting behaviors. As an important tool for behavior informatics, negative sequential patterns (NSP) (such as missing medical treatments) are critical and sometimes much more informative than positive sequential patterns (PSP) (e.g. ![]()
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