• The Journal of Korean Association of Computer Education
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• v.19 no.4
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• pp.59-66
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• 2016

It has been studied to reflect the number of co-rated items for solving data sparsity problem in collaborative filtering systems. A well-known method of Jaccard index allowed performance improvement, when combined with previous similarity measures. However, the degree of performance improvement when combined with existing similarity measures in various data environments are seldom analyzed, which is the objective of this study. Jaccard index as a sole similarity measure yielded much higher prediction quality than traditional measures and very high recommendation quality in a sparse dataset. In general, previous similarity measures combined with Jaccard index improved performance regardless of dataset characteristics. Especially, cosine similarity achieved the highest improvement in sparse datasets, while similarity of Mean Squared Difference degraded prediction quality in denser sets. Therefore, one needs to consider characteristics of data environment and similarity measures before combining Jaccard index for similarity use.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.05a
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• pp.36-39
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• 2019

데이터 마이닝에서 클러스터링은 서로 유사한 특징을 갖는 데이터들을 동일한 클래스로 분류하는 방법이다. 클러스터링에는 다양한 방법이 존재하지만 대표적으로 집합으로 표현된 데이터들의 유사도를 측정하기 위해서는 자카드 유사도(Jaccard Similarity)를 이용한다. 자카드 유사도는 서로 다른 집합 간의 공통된 부분을 상대적으로 평가하여 유사도를 측정하는 방법이다. 그러나 최근에는 데이터를 저장할 수 있는 기술과 매체의 발전으로 표현할 수 있는 데이터의 영역과 범위는 발전되고 있기 때문에 많은 연산과 시간의 비용이 발생하게 된다. 이를 해결하기 위해서 두 데이터의 표본의 유사도를 통해 실제 데이터들의 유사도를 추정할 수 있는 Min-Hash 가 제안되었다. 본 논문에서는 이를 활용하여 집합의 영역을 다중 집합(Multiset)으로 확장하여 중복되는 값을 가질 수 있는 두 데이터 간의 유사도를 효율적으로 추정할 수 있는 Min-Max Hash 를 제안한다.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.163-170
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• 2023

The user-based collaborative filtering technique, one of the implementation methods of the recommendation system, recommends the preferred items of neighboring users based on the calculations of neighboring users with similar rating histories. However, it fundamentally has a data scarcity problem in which the quality of recommendations is significantly reduced when there is little common rating history. To solve this problem, many existing studies have proposed various methods of combining Jaccard index with a similarity measure. In this study, we introduce a time-aware concept to Jaccard index and propose a method of weighting common items with different weights depending on the rating time. As a result of conducting experiments using various performance metrics and time intervals, it is confirmed that the proposed method showed the best performance compared to the original Jaccard index at most metrics, and that the optimal time interval differs depending on the type of performance metric.

• Journal of the Korea Society of Computer and Information
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• v.26 no.5
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• pp.47-53
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• 2021

Sparse ratings data hinder reliable similarity computation between users, which degrades the performance of memory-based collaborative filtering techniques for recommender systems. Many works in the literature have been developed for solving this data sparsity problem, where the most simple and representative ones are the methods of utilizing Jaccard index. This index reflects the number of commonly rated items between two users and is mostly integrated into traditional similarity measures to compute similarity more accurately between the users. However, such integration is very straightforward with no consideration of the degree of data sparsity. This study suggests a novel idea of applying different similarity measures depending on the numeric value of Jaccard index between two users. Performance experiments are conducted to obtain optimal values of the parameters used by the proposed method and evaluate it in comparison with other relevant methods. As a result, the proposed demonstrates the best and comparable performance in prediction and recommendation accuracies.

• Journal of the Korean Society for information Management
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• v.21 no.1
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• pp.93-117
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• 2004

This study is to develop a hierarchic clustering model fur document classification and browsing in OPAC systems. Two automatic indexing techniques (with and without controlled terms), two term weighting methods (based on term frequency and binary weight), five similarity coefficients (Dice, Jaccard, Pearson, Cosine, and Squared Euclidean). and three hierarchic clustering algorithms (Between Average Linkage, Within Average Linkage, and Complete Linkage method) were tested on the document collection of 175 books and theses on library and information science. The best document clusters resulted from the Between Average Linkage or Complete Linkage method with Jaccard or Dice coefficient on the automatic indexing with controlled terms in binary vector. The clusters from Between Average Linkage with Jaccard has more likely decimal classification structure.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.213-216
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• 2021

기술이 발전함에 따라 악성코드 또한 함께 발전하여 보안 위협이 증가되고 있다. 특히 PowerShell 과 같은 스크립트 언어를 사용하여 포렌식이 어려운 Fileless 악성코드가 지속적으로 증가하고 있다. 이에 본 논문에서 PowerShell 데이터셋을 활용하여 기존 패턴은 탐지할 수 없는 한계점을 가진 시그니처 또는 휴리스틱 기반 탐지 기법을 보완하여 기존의 악성코드들을 학습 및 새로운 악성코드를 추측하는 것이 가능한 심층 학습 기술, Graph Convolutional Networks 과 자카드 유사도를 활용하여 기존의 방식에 비해 더 효율적으로 탐지를 이루어 내는지를 판단해보려한다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.07a
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• pp.699-700
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• 2023

최근 국내 기업에서는 블라인트 테스트나 포트폴리오와 같은 자료를 활용하여 채용하는 추세이다. 지원자마다 개인의 역량이 다를 뿐만 아니라 기업에서 요구하는 기술/경험, 지원 자격, 특정 기술에 대한 경험을 요구한다. 따라서 본 논문에서는 국내 기업의 채용 공고에 기재된 지원 자격, 우대 기술, 우대 사항 등의 데이터와 지원자의 개인 역량(기술 스택, 전공 역량, 진행 프로젝트 등) 데이터를 활용하여 키워드를 추출한다. 지원자와 기업이 입력한 데이터를 통해 추출한 키워드들을 두 개의 집합으로 나눈 뒤 각각의 키워드를 할당한다. 할당받은 집합들을 비교하여 지원자의 정보가 기업의 채용 조건에 얼마나 부합하는지 계산한 후, 해당확률을 지원자에게 제공하는 방식의 시스템이다.

• Journal of Practical Engineering Education
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• v.14 no.3
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• pp.565-572
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• 2022

Card sorting is a useful data collection method for understanding users' perceptions of relationships between items. In general, card sorting is an intuitive and cost-effective technique that is very useful for user research and evaluation. In this study, the core competencies of each field were used as competency cards used in the next stage of card sorting for course development, and the clustering results were derived by applying the K-means algorithm to cluster the results. As a result of card sorting, competency clustering for core competencies for each occupation in each field was verified based on Participant-Centric Analysis (PCA). For the number of core competency cards for each occupation, the number of participants who agreed appropriately for clustering and the degree of card similarity were derived compared to the number of sorting participants.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.27 no.1
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• pp.87-96
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• 2023

As the number of security vulnerabilities increases yearly, security threats continue to occur, and the vulnerability risk is also important. We devise a security threat score calculation reflecting trends to determine the risk of security vulnerabilities. The three stages considered key elements such as attack type, supplier, vulnerability trend, and current attack methods and techniques. First, it reflects the results of checking the relevance of the attack type, supplier, and CVE. Secondly, it considers the characteristics of the topic group and CVE identified through the LDA algorithm by the Jaccard similarity technique. Third, the latest version of the MITER ATT&CK framework attack method, technology trend, and relevance between CVE are considered. We used the data within overseas sites provide reliable security information to review the usability of the proposed final formula CTRS. The scoring formula makes it possible to fast patch and respond to related information by identifying vulnerabilities with high relevance and risk only with some particular phrase.

• Journal of the Korean Society for Library and Information Science
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• v.38 no.1
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• pp.35-50
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• 2004

This study evaluated the applicability of the static hierarchic clustering model to clustering query results in OPAC. Two clustering methods(Between Average Linkage(BAL) and Complete Linkage(CL)) and two similarity coefficients(Dice and Jaccard) were tested on the query results retrieved from 16 title-based keyword searchings. The precision of optimal dusters was improved more than 100% compared with title-word searching. There was no difference between similarity coefficients but clustering methods in optimal cluster effectiveness. CL method is better in precision ratio but BAL is better in recall ratio at the optimal top-level and bottom-level clusters. However the differences are not significant except higher recall ratio of BAL at the top-level duster. Small number of clusters and long chain of hierarchy for optimal cluster resulted from BAL could not be desirable and efficient.