This study utilizes association rule learning and clustering analysis to explore the co-occurrence and relationships within ecosystems, focusing on the endangered brackish-water snail Clithon retropictum, classified as Class II endangered wildlife in Korea. The goal is to analyze co-occurrence patterns between brackish-water snails and other species to better understand their roles within the ecosystem. By examining co-occurrence patterns and relationships among species in large datasets, association rule learning aids in identifying significant relationships. Meanwhile, K-means and hierarchical clustering analyses are employed to assess ecological similarities and differences among species, facilitating their classification based on ecological characteristics. The findings reveal a significant level of relationship and co-occurrence between brackish-water snails and other species. This research underscores the importance of understanding these relationships for the conservation of endangered species like C. retropictum and for developing effective ecosystem management strategies. By emphasizing the role of a data-driven approach, this study contributes to advancing our knowledge on biodiversity conservation and ecosystem health, proposing new directions for future research in ecosystem management and conservation strategies.

본 연구는 동시출현단어(co-word) 분석을 이용하여 기술경영 분야의 연구 주제 네트워크를 구축하고, 핵심 연구 주제 및 연구 주제 간 상호연관관계를 도출한다. 동시출현 빈도수의 정규화를 통해 키워드 간 유사성을 도출하여 무방향 네트워크를 분석하는 기존연구들과는 달리 본 연구는 연관규칙분석(association rule)을 통해 키워드 간 신뢰도(confidence)를 도출하여 유방향 네트워크 분석을 수행한다. 2011～2014년 기술경영 분야 9 개 국제 학술지에 게재된 2,456개의 논문의 저자키워드를 대상으로 빈도수 상위 200개 키워드를 추출하고, 주제(THEME), 방법(METHOD), 분야(FIELD)의 세 가지 유형으로 키워드를 분류한다. 각 유형별 일원(one-mode) 네트워크를 구축하여, 함께 많이 연구가 이루어진키워드들을 찾아내고, 핵심 키워드를 도출한다. 또한 두 가지 유형의 키워드 간의 이원(two-mode) 네트워크를 구축하여, 연구 주제별로 함께 많이 활용된 방법 및 대상 분야를 탐색한다. 본 연구 결과는 최근 성숙기에 접어든 기술경영 분야의 연구 흐름 및 지식 구조를 키워드 수준에서 구체적으로 제시함으로써, 기술경영 분야 연구자들의 연구 주제 탐색 및 연구방향 설계에 활용될 수 있을 것으로 기대된다.

Since the Free Trade Agreements (FTAs) with Chile, the EU, and the U.S., Korean agricultural produce markets have turned into a fierce competition landscape. Under these competitive circumstances, marketing is critical. The objective of the research presented herein is to understand the characteristics of customer preferences after locating trends of purchased items. So This research establishes sustainable strategies for Korean agricultural produce. This investigation used market-basket analysis techniques and panel data for its research. Market-basket analysis is a technique which attempts to find groups of items that are commonly found together. The results show that, for one year, processed food using wheat, processed marine products, and pork are commonly bought together and that yogurt and milk also are bought together. The characteristics of customers buying these items are 44 years old and live in a four-person household with two children. These customers do not live with their parents.

When customers purchase a product, the process of searching for any purchase pattern process is called ‘Association Rule’. For using of this, if the customers is using unit of spare parts and the stores of displaying and selling the goods are the facility unit of having the spare parts, it will be represented that the demend pattern through the sales list in facility units. Current ASL(Authorized Stockage List) selection is the way of using the result analyzed actual experience used spare parts during the Korea War. it is specified that ASL selection criterion and procedures based on Army regulations and field manuals. This method is not conducted the association analysis between spare parts used the current equipment operating, and have not the clear criterion and analysis system about the ASL selection. In this study, in order to solve these problems, it was carried out the Association Rule targeting the sales list of the spare parts in point of between the using unit and products of occurred month based on the table designed the star-schema. And it is studied and proposed that the ASL selection way using the analysis result.

  A process mining is considered to support the discovery of business process for unstructured process model, and a process mining algorithm by using the associated rule and sequence pattern of data mining is developed to extract information about process

We adapted association rules of data mining in order to investigate the relation among the factors of musculoskeletal disorders and proposed the method of preventing the musculoskeletal disorders associated with multiple logistic regression in previous study. This multiple logistic regression was difficult to establish the method of preventing musculoskeletal disorders in case factors can't be managed by worker himself, i.e., age, gender, marital status. In order to solve this problem, we devised association rules of factors of musculoskeletal disorders and proposed the interactive method of preventing the musculoskeletal disorders, by applying association rules with the result of multiple logistic regression in previous study. The result of correlation analysis showed that prevention method of one part also prevents musculoskeletal disorders of other parts of body.

  We can offer suitable information to users analyzing the pattern of users. An association rule is one of data mining techniques which can discover the pattern. We use an association rule which considers the web page visiting time and we should the patte

Users who use Web site wish to get information conveniently. To users who web site operators use Web site differentiation to provide done service pattern analysis by user do must. Association rule is one of data Mining techniques for pattern discovery. If search for pattern by user, differentiation by user done service offer can. Association rule search result that pattern by user can know, and considers web page visiting time for association rule search differentiation done web structure service and recommendation service possible.

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.