• Proceedings of the Korean Information Science Society Conference
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• 2002.04b
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• pp.136-139
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• 2002

연관규칙 마이닝과정에서의 빈발항목 탐색의 대표적인 방법으로 알려진 Apriori 알고리즘의 성능을 향상시키기 위한 많은 연구가 진행되어 왔다. 본 논문에서는 트랜잭션 데이터베이스(TDB)에서 생성되는 각 패스의 k-itemset들에 대해 각각 트랜잭션 ID List(TIDist)를 유지하고 이를 이용해 (k+1)-itemset을 효율적으로 찾아내는 방법을 제안한다. 이 방법은 frequent (k+1)-itemset(k>0)의 빈도수 및 TIDList를 TDB 에 대한 스캔이 전혀 없이 k-itemset의 TIDList로부터 직접 구한다. 이는 빈발항목집합을 찾기 위한 탐색 complexity는 크게 줄여줄 뿐 아니라 시간 변화에 따른 빈발항목집합의 분포 정보를 제공해 준다.

• Proceedings of the Safety Management and Science Conference
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• 2000.11a
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• pp.299-306
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• 2000

General definition of data mining is the knowledge discovery or is to extract hidden necessary information from large databases. Its technique can be applied into decision making, prediction, and information analysis through analyzing of relationship and pattern among data. One of the most important work is to find association rules in data mining. The objective of this paper is to find customer's trend using association rule from analysis of database and the result can be used as fundamental data for CRM(Customer Relationship Management). This paper uses Apriori algorithm and FoodMart data in order to find association rules.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.05a
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• pp.33-35
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• 2019

서로 다른 아이템 집합의 연관성을 분석하는 것을 연관규칙분석이라 한다. 대표적인 알고리즘으로 Apriori 알고리즘이 있지만 DB스캔 횟수가 많아질 수 있고 후보 집합 생성으로 인해서 속도가 느려질 수 있다는 단점이 있다. 이를 효율적으로 개선한 FP-Growth 알고리즘을 구현하여 임의의 데이터를 이용하여 알고리즘의 속도에 대해 연구한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2008.11a
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• pp.336-339
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• 2008

우리가 사용하는 데이터베이스 내에는 많은 양의 데이터 들이 들어 있으며, 계속적으로 그 양은 늘어나고 있다. 이러한 데이터들로부터 질의를 통해 얻을 수 있는 기본적이고 단순한 정보들과 달리 고급 정보를 얻게 해주는 방법이 데이터 마이닝이다. 데이터 마이닝의 기법 중에서 본 논문에서는 k-means 알고리즘을 사용하여 트랜잭션을 클러스터링 함으로써 데이터베이스의 트랜잭션 수를 줄여 연관규칙의 대표적인 알고리즘인 Apriori 알고리즘의 단점인 트랜잭션 스캔으로 인한 성능 저하를 개선하고자 한다.

• Journal of Internet Computing and Services
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• v.10 no.5
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• pp.65-78
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• 2009

The most commonly adopted approach to find valuable information from tree data is to extract frequently occurring subtree patterns from them. Because mining frequent tree patterns has a wide range of applications such as xml mining, web usage mining, bioinformatics, and network multicast routing, many algorithms have been recently proposed to find the patterns. However, existing tree mining algorithms suffer from several serious pitfalls in finding frequent tree patterns from massive tree datasets. Some of the major problems are due to (1) modeling data as hierarchical tree structure, (2) the computationally high cost of the candidate maintenance, (3) the repetitious input dataset scans, and (4) the high memory dependency. These problems stem from that most of these algorithms are based on the well-known apriori algorithm and have used anti-monotone property for candidate generation and frequency counting in their algorithms. To solve the problems, we base a pattern-growth approach rather than the apriori approach, and choose to extract maximal frequent subtree patterns instead of frequent subtree patterns. The proposed method not only gets rid of the process for infrequent subtrees pruning, but also totally eliminates the problem of generating candidate subtrees. Hence, it significantly improves the whole mining process.

• Journal of Digital Convergence
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• v.20 no.4
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• pp.93-100
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• 2022

This study aimed to understand the current status of science and engineering university(SEU) R&D operations depending on the research project characteristics(e.g., stages and characteristics), then provide implications for future university R&D support systems and related policies. Hence, an online survey targeting SEU R&D recipients was conducted between October 4th to November 5th, 2021. Analyzing 445 valid data using the Apriori algorithm, 16 association rules for R&D operation according to the research project characteristics show that regardless of research characteristics, SEU's R&D projects, particularly in applied research, were funded or operated under the leadership of government or public institutions. For basic research, individual researchers had a higher level of autonomy in determining research topics; yet, they had a short duration (3 years) and a unit of evaluation period of more than 3 years. These findings can be empirical evidence for revealing the relationship among various variables in operating SEUs' R&D.

• Journal of the Korean Data and Information Science Society
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• v.17 no.2
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• pp.455-465
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• 2006

Data mining is the method to find useful information for large amounts of data in database. It is used to find hidden knowledge by massive data, unexpectedly pattern, and relation to new rule. The methods of data mining are decision tree, association rules, clustering, neural network and so on. We analyze industrial waste database using data mining technique. We use k-means algorithm for clustering and C5.0 algorithm for decision tree and Apriori algorithm for association rule. We can use these outputs for environmental preservation and environmental improvement.

• 한국데이터정보과학회:학술대회논문집
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• 2006.04a
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• pp.241-251
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• 2006

Data mining is the method to find useful information for large amounts of data in database It is used to find hidden knowledge by massive data, unexpectedly pattern, relation to new rule. The methods of data mining are decision tree, association rules, clustering, neural network and so on. We analyze industrial waste database using data mining technique. We use k-means algorithm for clustering and C5.0 algorithm for decision tree and Apriori algorithm for association rule. We can use these analysis outputs for environmental preservation and environmental improvement.

• Journal of Korea Society of Industrial Information Systems
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• v.17 no.5
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• pp.9-17
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• 2012

Merchandise recommendations service based on electronic commerce has been actively studied and on service these days. By virtue of progress in IT industry, POS has been widely used even in small shops, but the merchandise recommendations service using POS has not been much facilitated compared with that of using electronic commerce. This paper proposes a merchandise recommendations service system using association analysis by applying data mining algorithm to POS sales data. This paper, also, suggests novel services such as annihilation rule and new rule, and ascending and descending rules. The analysis results are applied to the customers enabling to offer merchandise recommendations service. In addition, prompt responses against the changes in demands from customers are possible by identifying the annihilation rule and new rule, and ascending and descending rules, and providing the management with the rules as managerial decision making information.

• Journal of Digital Convergence
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• v.13 no.9
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• pp.269-276
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• 2015

Recently, with the activation of the industry related to the big data, the global security companies have expanded their scopes from structured to unstructured data for the intelligent security threat monitoring and prevention, and they show the trend to utilize the technique of user's tendency analysis for security prevention. This is because the information scope that can be deducted from the existing structured data(Quantify existing available data) analysis is limited. This study is to utilize the analysis of security tendency(Items classified purpose distinction, positive, negative judgment, key analysis of keyword relevance) applying the machine learning algorithm($Na{\ddot{i}}ve$ Bayes, Decision Tree, K-nearest neighbor, Apriori) in the big data environment. Upon the capability analysis, it was confirmed that the security items and specific indexes for the decision of security tendency could be extracted from structured and unstructured data.