• Title/Summary/Keyword: K-Means 클러스터링

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A Clustering of Physical Fitness according to the Skeletal Maturation of Elementary School Students : Focused on Cluster Analysis (초등학생의 골성숙도에 따른 체력 군집화 : 군집분석 중심으로)

  • Kim, Dae-Hoon;Yoon, Hyoung-ki;Oh, Sei-Yi;Lee, Young-Jun;Cho, Seok-Yeon;Song, Dae-Sik;Seo, Dong-Nyeuck;Kim, Ju-Won;Na, Gyu-Min;Kim, Min-Jun;Oh, ․Kyung-A
    • Journal of the Korean Applied Science and Technology
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    • v.39 no.1
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    • pp.63-73
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    • 2022
  • The aim of this study was to cluster according to the bone age of elementary school students in order to analyze the physique, physical fitness, and skeletal maturation of each cluter group and to provide basic data for the balanced development of elementary school students through data analysis. The subjects of this study were 2243 students aged 8 to 13 years, and the skeletal maturation were calculated by applying them to the TW3 method score conversion table after the X-ray films were taken. A total of 2 components in physique were measured using a stadiometer(Hanebio, Korea, 2021) and the Inbody 270(Biospace, Korea, 2019), and a total of 7 components in physical fitness, which included muscular strength(Hand Grip Strength), balance(Bass Stick Test), agility(Plate Tapping), power(Standing Long Jump), flexibility(Sit&Reach), muscular endurance(Sit-Up), and cardiovascular endurance(Shuttle Run) were measured as well. K-Means clustering method, cross-tabulation analysis, and one-way variable analysis(ANOVA) were conducted for data processing using the SPSS PC/Program(Version 26.0) and Bristics Studio Tool, and it was considered significant at the level of p< .05. The results of this study may be summarized as follow. First, as a result of clustering using three components of skeletal maturation: retarded, normal, and advanced, cluster 1(Retarded) showed excellence in muscular strength, balance, and agility. cluster 2(Normal) showed poor flexibility, whereas cluster 3(Advanced) showed excellence in muscular strength. Second, as a result of analyzing the differences in physique according to the clustering of elementary school students by their individual characteristics, cluster 3(Advanced) showed excellence in height, weight, and body fat percentage. Third, as a result of analyzing the differences in physical fitness according to the clustering of elementary school students by their individual characteristics, cluster 3(Advanced) showed excellence in Hand Grip Strength(Left, Right), whereas cluster 1(Retarded) showed excellence in Bass Stick Test, and cluster 3(Advanced) showed excellence in Standing Long Jump.

Term Mapping Methodology between Everyday Words and Legal Terms for Law Information Search System (법령정보 검색을 위한 생활용어와 법률용어 간의 대응관계 탐색 방법론)

  • Kim, Ji Hyun;Lee, Jong-Seo;Lee, Myungjin;Kim, Wooju;Hong, June Seok
    • Journal of Intelligence and Information Systems
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    • v.18 no.3
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    • pp.137-152
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    • 2012
  • In the generation of Web 2.0, as many users start to make lots of web contents called user created contents by themselves, the World Wide Web is overflowing by countless information. Therefore, it becomes the key to find out meaningful information among lots of resources. Nowadays, the information retrieval is the most important thing throughout the whole field and several types of search services are developed and widely used in various fields to retrieve information that user really wants. Especially, the legal information search is one of the indispensable services in order to provide people with their convenience through searching the law necessary to their present situation as a channel getting knowledge about it. The Office of Legislation in Korea provides the Korean Law Information portal service to search the law information such as legislation, administrative rule, and judicial precedent from 2009, so people can conveniently find information related to the law. However, this service has limitation because the recent technology for search engine basically returns documents depending on whether the query is included in it or not as a search result. Therefore, it is really difficult to retrieve information related the law for general users who are not familiar with legal terms in the search engine using simple matching of keywords in spite of those kinds of efforts of the Office of Legislation in Korea, because there is a huge divergence between everyday words and legal terms which are especially from Chinese words. Generally, people try to access the law information using everyday words, so they have a difficulty to get the result that they exactly want. In this paper, we propose a term mapping methodology between everyday words and legal terms for general users who don't have sufficient background about legal terms, and we develop a search service that can provide the search results of law information from everyday words. This will be able to search the law information accurately without the knowledge of legal terminology. In other words, our research goal is to make a law information search system that general users are able to retrieval the law information with everyday words. First, this paper takes advantage of tags of internet blogs using the concept for collective intelligence to find out the term mapping relationship between everyday words and legal terms. In order to achieve our goal, we collect tags related to an everyday word from web blog posts. Generally, people add a non-hierarchical keyword or term like a synonym, especially called tag, in order to describe, classify, and manage their posts when they make any post in the internet blog. Second, the collected tags are clustered through the cluster analysis method, K-means. Then, we find a mapping relationship between an everyday word and a legal term using our estimation measure to select the fittest one that can match with an everyday word. Selected legal terms are given the definite relationship, and the relations between everyday words and legal terms are described using SKOS that is an ontology to describe the knowledge related to thesauri, classification schemes, taxonomies, and subject-heading. Thus, based on proposed mapping and searching methodologies, our legal information search system finds out a legal term mapped with user query and retrieves law information using a matched legal term, if users try to retrieve law information using an everyday word. Therefore, from our research, users can get exact results even if they do not have the knowledge related to legal terms. As a result of our research, we expect that general users who don't have professional legal background can conveniently and efficiently retrieve the legal information using everyday words.

Analysis of shopping website visit types and shopping pattern (쇼핑 웹사이트 탐색 유형과 방문 패턴 분석)

  • Choi, Kyungbin;Nam, Kihwan
    • Journal of Intelligence and Information Systems
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    • v.25 no.1
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    • pp.85-107
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    • 2019
  • Online consumers browse products belonging to a particular product line or brand for purchase, or simply leave a wide range of navigation without making purchase. The research on the behavior and purchase of online consumers has been steadily progressed, and related services and applications based on behavior data of consumers have been developed in practice. In recent years, customization strategies and recommendation systems of consumers have been utilized due to the development of big data technology, and attempts are being made to optimize users' shopping experience. However, even in such an attempt, it is very unlikely that online consumers will actually be able to visit the website and switch to the purchase stage. This is because online consumers do not just visit the website to purchase products but use and browse the websites differently according to their shopping motives and purposes. Therefore, it is important to analyze various types of visits as well as visits to purchase, which is important for understanding the behaviors of online consumers. In this study, we explored the clustering analysis of session based on click stream data of e-commerce company in order to explain diversity and complexity of search behavior of online consumers and typified search behavior. For the analysis, we converted data points of more than 8 million pages units into visit units' sessions, resulting in a total of over 500,000 website visit sessions. For each visit session, 12 characteristics such as page view, duration, search diversity, and page type concentration were extracted for clustering analysis. Considering the size of the data set, we performed the analysis using the Mini-Batch K-means algorithm, which has advantages in terms of learning speed and efficiency while maintaining the clustering performance similar to that of the clustering algorithm K-means. The most optimized number of clusters was derived from four, and the differences in session unit characteristics and purchasing rates were identified for each cluster. The online consumer visits the website several times and learns about the product and decides the purchase. In order to analyze the purchasing process over several visits of the online consumer, we constructed the visiting sequence data of the consumer based on the navigation patterns in the web site derived clustering analysis. The visit sequence data includes a series of visiting sequences until one purchase is made, and the items constituting one sequence become cluster labels derived from the foregoing. We have separately established a sequence data for consumers who have made purchases and data on visits for consumers who have only explored products without making purchases during the same period of time. And then sequential pattern mining was applied to extract frequent patterns from each sequence data. The minimum support is set to 10%, and frequent patterns consist of a sequence of cluster labels. While there are common derived patterns in both sequence data, there are also frequent patterns derived only from one side of sequence data. We found that the consumers who made purchases through the comparative analysis of the extracted frequent patterns showed the visiting pattern to decide to purchase the product repeatedly while searching for the specific product. The implication of this study is that we analyze the search type of online consumers by using large - scale click stream data and analyze the patterns of them to explain the behavior of purchasing process with data-driven point. Most studies that typology of online consumers have focused on the characteristics of the type and what factors are key in distinguishing that type. In this study, we carried out an analysis to type the behavior of online consumers, and further analyzed what order the types could be organized into one another and become a series of search patterns. In addition, online retailers will be able to try to improve their purchasing conversion through marketing strategies and recommendations for various types of visit and will be able to evaluate the effect of the strategy through changes in consumers' visit patterns.