경상남도 아파트 인터넷 시세 자료 분석

조광현,박희창 한국자료분석학회 2011 Journal of the Korean Data Analysis Society Vol.13 No.6

The construction of IT infra has brought a lot of changes to real estate as well as sudden increase of internet user number. Recently, imbalance problem of demand and supply is realized for uncertainty factor of the house market. Activation of real estate information network is considered actively. Many specialty companies which supply real estate information by increase of internet population made. But, application of real estate data and value of information by various problems are devalued. Therefore, we need various methods such as data mining about real estate data. Purpose of this study wish to apply normal statistical methods and data mining rule for apartment price data in Gyeongnam. This study is a basic research about application of internet real estate information. Also, this study may utilize to basic data to establish analysis method of internet real estate information and analysis application direction etc. IT 인프라 구축은 인터넷 사용자 수의 급격한 증가뿐만 아니라 부동산 분야에도 많은 변화를 가져왔다. 최근 수요와 공급의 불균형 문제가 주택시장의 불안요인으로 인식되고 있으며, 부동산 정보망의 활성화가 적극적으로 고려되고 있다. 현재 부동산 정보를 제공하는 많은 전문 업체들이 생겨났으나, 부동산 자료의 활용 및 정보의 가치가 저평가 되고 있어 데이터마이닝 등의 다양한 분석 방법의 적용이 필요하다. 이에 본 논문에서는 경상남도의 아파트 시세 자료에 대하여 일반적인 통계 기법뿐만 아니라 데이터마이닝 등을 적용하고자 한다. 본 연구의 결과, 일반적인 통계분석을 통하여 경상남도 아파트 시세에 대한 경향을 알 수 있었고, 연관성 규칙 적용으로 아파트 시세와 관련된 변수들을 찾을 수 있었으며, 의사결정나무와 군집 분석을 통하여 아파트 분류 및 그 속성을 파악할 수 있었다. 본 연구는 인터넷 부동산 정보의 활용에 대한 기초 연구라고 할 수 있으며, 인터넷 부동산 정보의 분석 방법과 분석 활용 방향 등을 수립하기 위한 기초 자료로 활용할 수 있을 것이다.

다중외적연관성규칙을 이용한 불필요한 입력변수 제거에 관한 연구

조광현,박희창,Cho, Kwang-Hyun,Park, Hee-Chang 한국데이터정보과학회 2011 한국데이터정보과학회지 Vol.22 No.5

The decision tree is a representative algorithm of data mining and used in many domains such as retail target marketing, fraud detection, data reduction, variable screening, category merging, etc. This method is most useful in classification problems, and to make predictions for a target group after dividing it into several small groups. When we create a model of decision tree with a large number of input variables, we suffer difficulties in exploration and analysis of the model because of complex trees. And we can often find some association exist between input variables by external variables despite of no intrinsic association. In this paper, we study on the removal method of unnecessary input variables using multiple external association rules. And then we apply the removal method to actual data for its efficiencies.

연관성규칙에서 의미 없는 규칙의 발견에 관한 연구

조광현,박희창,Cho, Kwang-Hyun,Park, Hee-Chang 한국데이터정보과학회 2011 한국데이터정보과학회지 Vol.22 No.1

연관성규칙은 대용량 데이터베이스에서 각 항목들 간의 관련성을 찾아내는 기법으로 둘 또는 그 이상의 품목들 사이의 지지도, 신뢰도, 향상도를 바탕으로 관련성 여부를 측정한다. 연관성규칙에서는 일반적으로 사용하는 연관성규칙 이외에 연관성규칙의 효율성을 개선하기 위하여 여러 가지 제약기반 연관성규칙의 연구가 활발하게 진행되고 있다. 연관성규칙 생성 시, 종종 많은 규칙들을 발견할 수 있다. 이는 변수들 간에 우연히 관련성이 높게 나타나는 경우가 존재할 수 있고 매개변수에 의하여 직접적인 관련성이 없는 규칙을 발견할 수도 있다. 이에 본 논문에서는 연관성규칙에서 매개변수에 의한 의미 없는 규칙의 발견에 관하여 연구하고자 한다. 본 연구 결과는 연관성 규칙에서 생성된 규칙에 대한 관련성을 보다 정확하게 이해할 수 있도록 함으로써 결과의 해석을 보다 명확하게 할 수 있다.

매개 변수를 이용한 의사결정나무 생성에 관한 연구

조광현,박희창,Cho, Kwang-Hyun,Park, Hee-Chang 한국데이터정보과학회 2011 한국데이터정보과학회지 Vol.22 No.4

Data mining searches for interesting relationships among items in a given database. The methods of data mining are decision tree, association rules, clustering, neural network and so on. The decision tree approach is most useful in classification problems and to divide the search space into rectangular regions. Decision tree algorithms are used extensively for data mining in many domains such as retail target marketing, customer classification, etc. When create decision tree model, complicated model by standard of model creation and number of input variable is produced. Specially, there is difficulty in model creation and analysis in case of there are a lot of numbers of input variable. In this study, we study on decision tree using intervening variable. We apply to actuality data to suggest method that remove unnecessary input variable for created model and search the efficiency.

기업의 지적재산관리, 어떻게 할 것인가?(1)

조광현,Jo, Gwang-Hyeon 한국발명진흥회 2008 發明特許 Vol.33 No.8

사회지표조사에서의 3단계 복합 데이터마이닝의 적용 방안

조광현,박희창,Cho, Kwang-Hyun,Park, Hee-Chang 한국데이터정보과학회 2012 한국데이터정보과학회지 Vol.23 No.5

Social indicator survey can identify the state of society as a whole. When we create a policy, social indicator survey can reflect the public opinion of the region. Social indicator survey is an important measure of social change. Social indicator survey has been conducted in many municipalities (Seoul, Incheon, Busan, Ulsan, Gyeongsangnamdo, etc.). But, the result of social indicator survey analysis is mainly the basic statistical analysis. In this study, we propose a new data mining methodology for effective analysis. We propose a 3-step complex data mining in society indicator survey. 3-step complex data mining uses three data mining method (intervening association rule, clustering, decision tree).

주변조건부 변수를 이용한 의사결정나무모형 생성에 관한 연구

조광현,박희창,Cho, Kwang-Hyun,Park, Hee-Chang 한국데이터정보과학회 2012 한국데이터정보과학회지 Vol.23 No.2

Data mining is a method of searching for an interesting relationship among items in a given database. The decision tree is a typical algorithm of data mining. The decision tree is the method that classifies or predicts a group as some subgroups. In general, when researchers create a decision tree model, the generated model can be complicated by the standard of model creation and the number of input variables. In particular, if the decision trees have a large number of input variables in a model, the generated models can be complex and difficult to analyze model. When creating the decision tree model, if there are marginally conditional variables (intervening variables, external variables) in the input variables, it is not directly relevant. In this study, we suggest the method of creating a decision tree using marginally conditional variables and apply to actual data to search for efficiency.

주변 조건부 변수에 의한 연관성 규칙 생성에 관한 연구

조광현,박희창,Cho, Kwang-Hyun,Park, Hee-Chang 한국데이터정보과학회 2012 한국데이터정보과학회지 Vol.23 No.1

Association rule mining searches for interesting relationships among items in a given database. Currently, study of the constraint-based association rules are underway by many researchers. When we create relation rule, we can often find a lot of rules. Of this rules, we can find rule that direct relativity by marginally conditional variables (intervening variable, external variable) does not exist. In such a case, this association rule can be considered insignificant. In this study, we want to study for association rules creation using marginally conditional variable. The result of this study can find meaningless association rules. Also, we can understand more exactly the relationships between variables.

RFMP를 이용한 부동산 회원 분류에 관한 연구

조광현,박희창,Cho, Kwang-Hyun,Park, Hee-Chang 한국데이터정보과학회 2012 한국데이터정보과학회지 Vol.23 No.3

Most companies make efforts to maximize their profitability by improving loyalty to existing customers through customer relationship management (CRM). According to the Wikipedia, CRM is a widely implemented strategy for managing a company's interactions with customers, clients and sales prospects. And RFM is a method used for analyzing customer behavior and defining market segments. It is commonly used in database marketing and direct marketing and has received particular attention in retail. In general, one considers recency, frequency, and monetary for customer segmentation in RFM method. In this paper, we apply RFMP method added to the purchase period of advertising items in the traditional RFM model for real estate customer segmentation. We will be able to establish the differentiated marketing strategy by RFMP method.

젖소군의 유생산 수준별 이질성 분산 보정

조광현,이준호,박경도,Cho, Kwang-Hyun,Lee, Joon-Ho,Park, Kyung-Do 한국데이터정보과학회 2014 한국데이터정보과학회지 Vol.25 No.4

This experiment was conducted to compare heterogeneity for the variance in dairy cattle population and to induce homogeneity of variance using 502,228 performance test records of dairy cattle. The estimates of heritability for milk yields, fat yields and protein yields were 0.28, 0.26 and 0.24, respectively and the estimate of average breeding value by birth year was lower in HV (heterogenous variance) model than in animal model, collectively. The average breeding values of milk yields, fat yields and protein yields for 545 sire bulls applicable to the criteria of interbull MACE programme were 453.54kg, 10.75kg and 14.33kg, respectively and when the heterogeneity was adjusted they were 432.06kg, 10.15kg and 13.40kg, respectively, which were lower in all milk traits collectively. In animal model, coefficients of phenotypic correlation between dataset I and II were 0.839 in milk yields, 0.821 in fat yields, and 0.837 in protein yields, while in HV model, they were 0.841 in milk yields, 0.820 in fat yields, and 0.836 in protein yields, showing similar results in 2 models. When compared using animal model and HV model, the regression coefficient for ratio of number of daughters by calving year of milk yields increased from 15.157 to 16.105 and that of fat yields increased from =0.227 to =0.196, but that of protein yields decreased from 0.630 to 0.586.