• Title/Summary/Keyword: Industrial Clustering Analysis

Search Result 185, Processing Time 0.027 seconds

Discovering Meaningful Trends in the Inaugural Addresses of United States Presidents Via Text Mining (텍스트마이닝을 활용한 미국 대통령 취임 연설문의 트렌드 연구)

  • Cho, Su Gon;Cho, Jaehee;Kim, Seoung Bum
    • Journal of Korean Institute of Industrial Engineers
    • /
    • v.41 no.5
    • /
    • pp.453-460
    • /
    • 2015
  • Identification of meaningful patterns and trends in large volumes of text data is an important task in various research areas. In the present study, we propose a procedure to find meaningful tendencies based on a combination of text mining, cluster analysis, and low-dimensional embedding. To demonstrate applicability and effectiveness of the proposed procedure, we analyzed the inaugural addresses of the presidents of the United States from 1789 to 2009. The main results of this study show that trends in the national policy agenda can be discovered based on clustering and visualization algorithms.

Exploring Prospective Research Areas in UI/UX through the Analysis of Patents (특허분석을 통한 UI/UX(User Interface/User Experience) 분야의 유망 연구영역 탐색)

  • Lim, Chaeguk;Yun, Dooseob;Park, Inchae;Park, Gwangman;Koh, Soonju;Yoon, Byungun
    • Korean Management Science Review
    • /
    • v.32 no.4
    • /
    • pp.1-18
    • /
    • 2015
  • On UX/UI fields, understanding emerging technology is important for preoccupancy of a future market. Most of emerging field searching methods are qualitative methods. However, it is suitable for large companies to adduce its milestone, not for small enterprises. Thus, this study aims at purposing the improvement of utilizability for research field searching processes. We draw core patents with modified patent citation data, apply the Girvan-Newman clustering method based on bibliographic coupling patent relationship and then, draw emerging technology research UX/UI fields. Finally, the results were validated in comparison with a report on emerging research.

Meta Analysis of Usability Experimental Research Using New Bi-Clustering Algorithm

  • Kim, Kyung-A;Hwang, Won-Il
    • The Korean Journal of Applied Statistics
    • /
    • v.21 no.6
    • /
    • pp.1007-1014
    • /
    • 2008
  • Usability evaluation(UE) experiments are conducted to provide UE practitioners with guidelines for better outcomes. In UE research, significant quantities of empirical results have been accumulated in the past decades. While those results have been anticipated to integrate for producing generalized guidelines, traditional meta-analysis has limitations to combine UE empirical results that often show considerable heterogeneity. In this study, a new data mining method called weighted bi-clustering(WBC) was proposed to partition heterogeneous studies into homogeneous subsets. We applied the WBC to UE empirical results and identified two homogeneous subsets, each of which can be meta-analyzed. In addition, interactions between experimental conditions and UE methods were hypothesized based on the resulting partition and some interactions were confirmed via statistical tests.

Construction of Observational Locations for Measuring Water Quality in the River Area (하천유역 수질 관측망 구성 연구)

  • Kwon, S.H.;Oh, H.S.
    • Journal of Korean Society of Industrial and Systems Engineering
    • /
    • v.35 no.3
    • /
    • pp.187-191
    • /
    • 2012
  • The methods for constructing network of observational locations for measuring water quality in water reservoirs have been widely proposed, but they had some limitations to be applied to river areas, which lie in awkward clustering and finding representative observational locations among locations within each clustering. In this paper, a statistical approach to detect anomaly locations which were significantly different in important measurements for the water quality from the previous locations and construct observational network with them was proposed. Anomaly was detected with the sampling distribution of each primary principal component score, sum of primary PCs, or sum of residual PCs. The empirical study with the data of Nakdong Dam for guiding how to use our proposed approach and showing limitations of previous studied was described.

Reduction of Simulation Number for Ship Handling Safety Assessment (선박운항 시뮬레이터 실험조건 축소화 연구)

  • Kwon, S.H.;Oh, H.S.
    • Journal of Korean Society of Industrial and Systems Engineering
    • /
    • v.35 no.1
    • /
    • pp.101-106
    • /
    • 2012
  • Ship handling simulator is a virtual ship navigating system with three dimensional screen system and simulation programs. FTS simulation can produce theoretically infinite experiment tests without time constraint, but which results in collecting determinstic observations. RTS simulation can collect statistical observations but has disadvantage of spending at least 30 minutes for a single experiment. The previous studies suggested that the number of experiment conditions to be tested could be reduced to obtain random data with RTS simulation by focusing on highly difficult experiment condition for ship handling. It has the limitation of not estimating the distribution of ship handling difficulty for the route. In this paper, similarity and clustering analysis are suggested for reduction methodology of experiment conditions. Similarity of experiment conditions are measured as follows: euclidean distance of ship handling difficulty index and correlation matrix of distance differences from the designed route. Clustering analysis and multi-dimensional scaling are applied to classify experiment conditions with measured similarity into reducing the number of RTS simulation conditions. An empirical result on Dangin harbor is shown and discussed.

Clustering Analysis of Films on Box Office Performance : Based on Web Crawling (영화 흥행과 관련된 영화별 특성에 대한 군집분석 : 웹 크롤링 활용)

  • Lee, Jai-Ill;Chun, Young-Ho;Ha, Chunghun
    • Journal of Korean Society of Industrial and Systems Engineering
    • /
    • v.39 no.3
    • /
    • pp.90-99
    • /
    • 2016
  • Forecasting of box office performance after a film release is very important, from the viewpoint of increase profitability by reducing the production cost and the marketing cost. Analysis of psychological factors such as word-of-mouth and expert assessment is essential, but hard to perform due to the difficulties of data collection. Information technology such as web crawling and text mining can help to overcome this situation. For effective text mining, categorization of objects is required. In this perspective, the objective of this study is to provide a framework for classifying films according to their characteristics. Data including psychological factors are collected from Web sites using the web crawling. A clustering analysis is conducted to classify films and a series of one-way ANOVA analysis are conducted to statistically verify the differences of characteristics among groups. The result of the cluster analysis based on the review and revenues shows that the films can be categorized into four distinct groups and the differences of characteristics are statistically significant. The first group is high sales of the box office and the number of clicks on reviews is higher than other groups. The characteristic of the second group is similar with the 1st group, while the length of review is longer and the box office sales are not good. The third group's audiences prefer to documentaries and animations and the number of comments and interests are significantly lower than other groups. The last group prefer to criminal, thriller and suspense genre. Correspondence analysis is also conducted to match the groups and intrinsic characteristics of films such as genre, movie rating and nation.

Feature Extraction of Welds from Industrial Computed Radiography Using Image Analysis and Local Statistic Line-Clustering (산업용 CR 영상분석과 국부확률 선군집화에 의한 용접특징추출)

  • Hwang, Jung-Won;Hwang, Jae-Ho
    • Journal of the Institute of Electronics Engineers of Korea SP
    • /
    • v.45 no.5
    • /
    • pp.103-110
    • /
    • 2008
  • A reliable extraction of welded area is the precedent task before the detection of weld defects in industrial radiography. This paper describes an attempt to detect and extract the welded features of steel tubes from the computed radiography(CR) images. The statistical properties are first analyzed on over 160 sample radiographic images which represent either weld or non-weld area to identify the differences between them. The analysis is then proceeded by pattern classification to determine the clustering parameters. These parameters are the width, the functional match, and continuity. The observed weld image is processed line by line to calculate these parameters for each flexible moving window in line image pixel set. The local statistic line-clustering method is used as the classifier to recognize each window data as weld or non-weld cluster. The sequential procedure is to track the edge lines between two distinct regions by iterative calculation of threshold, and it results in extracting the weld feature. Our methodology is concluded to be effective after experiment with CR weld images.

Forecasting Electric Power Demand Using Census Information and Electric Power Load (센서스 정보 및 전력 부하를 활용한 전력 수요 예측)

  • Lee, Heon Gyu;Shin, Yong Ho
    • Journal of Korea Society of Industrial Information Systems
    • /
    • v.18 no.3
    • /
    • pp.35-46
    • /
    • 2013
  • In order to develop an accurate analytical model for domestic electricity demand forecasting, we propose a prediction method of the electric power demand pattern by combining SMO classification techniques and a dimension reduction conceptualized subspace clustering techniques suitable for high-dimensional data cluster analysis. In terms of electricity demand pattern prediction, hourly electricity load patterns and the demographic and geographic characteristics can be analyzed by integrating the wireless load monitoring data as well as sub-regional unit of census information. There are composed of a total of 18 characteristics clusters in the prediction result for the sub-regional demand pattern by using census information and power load of Seoul metropolitan area. The power demand pattern prediction accuracy was approximately 85%.

FC Approach in Portfolio Selection of Tehran's Stock Market

  • Shadkam, Elham
    • The Journal of Asian Finance, Economics and Business
    • /
    • v.1 no.2
    • /
    • pp.31-37
    • /
    • 2014
  • The portfolio selection is one of the most important and vital decisions that a real or legal person, who invests in stock market, should make. The main purpose of this article is the determination of the optimal portfolio with regard to relations among stock returns of companies which are active in Tehran's stock market. For achieving this goal, weekly statistics of company's stocks since Farvardin 1389 until Esfand 1390, has been used. For analyzing statistics and information and examination of stocks of companies which has change in returns, factors analysis approach and clustering analysis has been used (FC approach). With using multivariate analysis and with the aim of reducing the unsystematic risk, a financial portfoliois formed. At last but not least, results of choosing the optimal portfolio rather than randomly choosing a portfolio are given.

Identifying the Diffusion Patterns of Movies by Opening Strength and Profitability (개봉 규모와 수익성에 따른 영화의 분류와 확산 패턴 분석)

  • Kim, Taegu;Hong, Jungsik
    • Journal of Korean Institute of Industrial Engineers
    • /
    • v.39 no.5
    • /
    • pp.412-421
    • /
    • 2013
  • Motion picture industry is one of the most representative fields in the cultural industry and has experienced constant growth both worldwide and within domestic markets. However, little research has been undertaken for diffusion patterns of motion pictures, whereas various issues such as demand forecasting and success factor analysis have been widely explored. To analyze diffusion patterns, we adopted extended Bass model to reflect the potential demand of movies. Four clusters of selected movies were derived by k-means clustering method with criteria of opening strength and profitability and then compared by their diffusion patterns. Results indicated that movies with high profitability and medium opening strength are most significantly influenced by word of mouth effect, while low profitability movies display nearly monotonic decreasing diffusion patterns with noticeable initial adoption rates and relatively early peak points in their runs.