• The Journal of the Korea institute of electronic communication sciences
• /
• v.13 no.1
• /
• pp.207-212
• /
• 2018

As a data analysis tool which is becoming popular in the Big Data era, R is rapidly expanding its user range by providing powerful statistical analysis and data visualization functions. Major advantage of R is its functional scalability based on open source, but its scale scalability is limited, resulting in performance degrades in large data processing. RHadoop, one of the extension packages to complement it, can improve data analysis performance as it supports Hadoop platform-based distributed processing of programs written in R. In this paper, we evaluate the validity of RHadoop by evaluating the performance improvement of RHadoop in real medical big data analysis. Performance evaluation of the analysis of the medical history information, which is provided by National Health Insurance Service, using R and RHadoop shows that RHadoop cluster composed of 8 data nodes can improve performance up to 8 times compared with R.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.35 no.5
• /
• pp.415-422
• /
• 2017

Finding influential factors from given clustering result is a typical data science problem. Genetic Algorithm based method is proposed to derive influential factors and its performance is compared with two conventional methods, Classification and Regression Tree (CART) and Chi-Squared Automatic Interaction Detection (CHAID), by using Dunn's index measure. To extract the influential factors of preference towards political parties in South Korea, the vote result of $18^{th}$ presidential election and 'Demographic', 'Health and Welfare', 'Economic' and 'Business' related data were used. Based on the analysis, reverse engineering was implemented. Implementation of reverse engineering based approach for influential factor analysis can provide new set of influential variables which can present new insight towards the data mining field.

• Journal of Fashion Business
• /
• v.9 no.2
• /
• pp.160-173
• /
• 2005

Fashion is hard to expect owing to the rapid change in accordance with consumer taste and environment, and has a tendency toward variety and individuality. Especially street fashion d 21st century is not being regarded as one of the subcultures but is playing an important role as a fountainhead d fashion trend. Therefore, Searching and analyzing street fashions helps us to understand the popular fashions d the next season and also it is important in understanding the consumer fashion sense and commercial area. So, we need to understand fashion styles quantitatively and qualitatively by providing visual data and dividing images. The purpose of this study is to design for street fashion on design analysis using web which can update quantitative and qualitative data. through the on site investigation d street fashion, and put the information onto a database.

• Journal of information and communication convergence engineering
• /
• v.17 no.1
• /
• pp.41-48
• /
• 2019

In recent years, sentiment analysis research has become popular. The research results of sentiment analysis have achieved remarkable results in practical applications, such as in Amazon's book recommendation system and the North American movie box office evaluation system. Analyzing big data based on user preferences and evaluations and recommending hot-selling books and hot-rated movies to users in a targeted manner greatly improve book sales and attendance rate in movies [1, 2]. However, traditional machine learning-based sentiment analysis methods such as the Classification and Regression Tree (CART), Support Vector Machine (SVM), and k-nearest neighbor classification (kNN) had performed poorly in accuracy. In this paper, an improved kNN classification method is proposed. Through the improved method and normalizing of data, the purpose of improving accuracy is achieved. Subsequently, the three classification algorithms and the improved algorithm were compared based on experimental data. Experiments show that the improved method performs best in the kNN classification method, with an accuracy rate of 11.5% and a precision rate of 20.3%.

• International Journal of Advanced Culture Technology
• /
• v.12 no.2
• /
• pp.448-454
• /
• 2024

This study provides academic implications by considering trends of domestic research regarding therapy for sensory integration intervention based on vestibular-proprioceptive system. For the analysis of this study, text mining with the use of R program and social network analysis method have been used and 53 papers have been collected. In conclusion, this study presents significant results as it provided basic rehabilitation data for sensory integration intervention based on vestibular-proprioceptive system through new research methods by analyzing with big data method by proposing the results through visualization from seeking research trends of sensory integration intervention based on vestibular-proprioceptive system through text mining and social network analysis.

• Journal of Korea Society of Industrial Information Systems
• /
• v.20 no.2
• /
• pp.113-124
• /
• 2015

Unlike structured data which are gathered and saved in a predefined structure, unstructured text data which are mostly written in natural language have larger applications recently due to the emergence of web 2.0. Text mining is one of the most important big data analysis techniques that extracts meaningful information in the text because it has not only increased in the amount of text data but also human being's emotion is expressed directly. In this study, we used R program, an open source software for statistical analysis, and studied algorithm implementation to conduct analyses (such as Frequency Analysis, Cluster Analysis, Word Cloud, Social Network Analysis). Especially, to focus on our research scope, we used keyword extract method based on a Data Dictionary. By applying in real cases, we could find that R is very useful as a statistical analysis software working on variety of OS and with other languages interface.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.34 no.1
• /
• pp.115-127
• /
• 2024

The recent increase in digital audio media has greatly expanded the size and diversity of sound data, which has increased the importance of sound data analysis in the digital forensics process. However, the lack of standardized procedures and guidelines for sound data analysis has caused problems with the consistency and reliability of analysis results. The digital environment includes a wide variety of audio formats and recording conditions, but current audio forensic methodologies do not adequately reflect this diversity. Therefore, this study identifies Life-Cycle-based sound data elemental technologies and provides overall guidelines for sound data analysis so that effective analysis can be performed in all situations. Furthermore, the identified elemental technologies were analyzed for use in the development of digital forensic techniques for sound data. To demonstrate the effectiveness of the life-cycle-based sound data elemental technology identification system presented in this study, a case study on the process of developing an emergency retrieval technology based on sound data is presented. Through this case study, we confirmed that the elemental technologies identified based on the Life-Cycle in the process of developing digital forensic technology for sound data ensure the quality and consistency of data analysis and enable efficient sound data analysis.

• Journal of the Korea Society of Computer and Information
• /
• v.22 no.6
• /
• pp.57-68
• /
• 2017

The Linked Open Data(LOD) cloud is quickly becoming one of the largest collections of interlinked datasets and the de facto standard for publishing, sharing and connecting pieces of data on the Web. Data publishers from diverse domains publish their data using Resource Description Framework(RDF) data model and provide SPARQL endpoints to enable querying their data, which enables creating a global, distributed and interconnected dataspace on the LOD cloud. Although it is possible to extract structured data as query results by using SPARQL, users have very poor in analysis and visualization of RDF data from SPARQL query results. Therefore, to tackle this issue, based on Formal Concept Analysis, we propose a novel approach for analyzing and visualizing useful information from the LOD cloud. The RDF data analysis and visualization technique proposed in this paper can be utilized in the field of semantic web data mining by extracting and analyzing the information and knowledge inherent in LOD and supporting classification and visualization.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2003.09a
• /
• pp.224-227
• /
• 2003

The Intrsuion Detecion Systems(IDS) are required the accuracy, the adaptability, and the expansion in the information society to be changed quickly. Also, it is required the more structured, and intelligent IDS to protect the resource which is important and maintains a secret in the complicated network environment. The research has the purpose to build the model for the intelligent IDS, which creates the intrusion patterns. The intrusion pattern has extracted from the vast amount of data. To manage the large size of data accurately and efficiently, the link analysis and sequence analysis among the data mining techniqes are used to build the model creating the intrusion patterns. The model is consist of "Time based Traffic Model", "Host based Traffic Model", and "Content Model", which is produced the different intrusion patterns with each model. The model can be created the stable patterns efficiently. That is, we can build the intrusion detection model based on the intelligent systems. The rules prodeuced by the model become the rule to be represented the intrusion data, and classify the normal and abnormal users. The data to be used are KDD audit data.

• Journal of Web Engineering
• /
• v.20 no.3
• /
• pp.795-816
• /
• 2021

The existing industry evaluation method utilizes the method of collecting the structured information such as the financial information of the companies included in the relevant industry and deriving the industrial evaluation index through the statistical analysis model. This method takes a long time to calculate the structured data and cause the time delay problem. In this paper, to solve this time delay problem, we derive monthly industry-specific interest and likability as a time series data type, which is a new industry evaluation indicator based on unstructured data. In addition, we propose a method to predict the industrial risk index, which is used as an important factor in industrial evaluation, based on derived industry-specific interest and likability time series data.