Earticle

In order to improve the low accuracy of traditional wireless network health information retrieval methods, a wireless network health information retrieval method is designed based on data mining algorithm. The invalid health information stored in wireless network is filtered by data mapping, and the health information is clustered by data mining algorithm. On this basis, the high-frequency words of health information are classified to realize wireless network health information retrieval. The experimental results show that exactitude of design way is significantly higher than that of the traditional method, which can solve the problem of low accuracy of the traditional wireless network health information retrieval method.

Data mining, which is attracting public attention, is a process of drawing out knowledge from a large mass of data. The key technique in data mining is the ability to maximize the similarity in a group and minimize the similarity between groups. Since grouping in data mining deals with a large mass of data, it lessens the amount of time spent with the source data, and grouping techniques that shrink the quantity of the data form to which the algorithm is subjected are actively used. The current grouping algorithm is highly sensitive to static and reacts to local minima. The number of groups has to be stated depending on the initialization value. In this paper we propose a gene algorithm that automatically decides on the number of grouping algorithms. We will try to find the optimal group of the fittest function, and finally apply it to a data mining problem that deals with a large mass of data.

농구대회는 선수들이 다양한 기본적인 농구 기술을 사용하여 특정한 전술적 조직 형태에 따라 공격과 수비를 바꾸 는 과정이다. 스포츠 분야에서는, 스포츠 훈련, 실제 격투 경기, 학교 스포츠 관리, 국민 체력 테스트에서 많은 양의 데이터 와 정보가 생성될 것이다. 농구 경기 과정에서 코치와 농구 관련 직원은 다양한 수단을 사용하여 라이벌 팀으로부터 데이터 를 수집하게 되는데, 일부는 직관적인 반면, 다른 팀들은 중요한 정보를 직접적으로 표시하지 못할 수도 있다. 데이터 마이닝은 복잡하고 수많은 데이터 리소스에서 현실과 일치하는 유용한 정보를 찾고 그 안에 숨겨진 정보와 지식을 추출하 는 과정입니다. 본 논문에서는 데이터 수집 및 적용과 농구 기술 조치의 사전 처리를 통해 데이터 마이닝 방법을 기반으로 농구 기술 조치 간의 상관 관계를 연구하였다.

Basketball competition is a process in which athletes use various basic basketball techniques to change offense and defense according to certain tactical organizational forms. In the field of sports, a large amount of data and information will be generated in sports training, actual combat competitions, school sports management and national physical fitness tests. In the process of basketball matches, coaches and basketball-related staff will use various means to collect data from rival teams, some of which are intuitive, while others may not be able to directly display their important information. Data mining is the process of finding useful information that is consistent with the reality from the complex and numerous data resources, and extracting the information and knowledge that are hidden in it. In this paper, through the analysis and application of data collection and preprocessing of basketball technical action, based on the data mining method, the correlation between basketball technical action is studied.

Data mining techniques develop a more accurate classification algorithm for patients classified as either normotensive, prehypertensive, or hypertensive. Logistic Model Tree, NBTree, and Bagging were chosen as the three classification models with tenfold cross-validation (LMT). Over 24 hours, we collected ABP readings from 1161 patients. To analyze the data, data mining techniques were used and a tool called WEKA. The data was analyzed based on age, gender, wake-up blood pressure, medication, sleep-up blood pressure, and overall blood pressure. According to bagging results, 886 cases (76.3 percent) are correctly classified, with 270 cases classified as pre-hypertensive, 436 cases as Normotensive, and 180 cases as hypertensive. NBTree's results show that 882 (75.9%) of the 1161 instances are correctly classified. Pre-hypertensive patients make up 256, normotensive patients 442, and hypertensive patients 184. Of the 1161 instances, the LMT algorithm correctly classified 878 (75.6 percent). According to the results, 275 people are pre-hypertensive, 431 are normotensive, and 172 are hypertensive. According to our findings, bagging is the most accurate classifier for the 24 hour ABP Monitoring dataset we used. Bagging achieves less overfitting because it focuses on global accuracy. It stabilizes and improves the accuracy of unstable methods compared to single classifiers.

Decision Tree is one of analysis techniques which conducts grouping and prediction into several sub-groups from interested groups. Researcher can easily understand this progress and explain than other techniques. Because Decision Tree is easy technique to see results. This paper uses CART algorithm which is one of data mining technique. It used 273 variables and 70094 data(2010-2011) of working environment survey conducted by Korea Occupational Safety and Health Agency(KOSHA). And then refines this data, uses final 12 variables and 35447 data. To find satisfaction factor in working environment, this page has grouped employee to 3 types (under 30 age, 30 ~ 49age, over 50 age) and analyzed factor. Using CART algorithm, finds the best grouping variables in 155 data. It appeared that ‘comfortable in organization’ and ‘proper reward’ is the best grouping factor.

본 논문에서는 데이터 마이닝에 필요한 클러스터링과정에서 불필요한 정보를 감축하기 위하여 베이지언 사 후확률의 신뢰도를 이용한 새로운 척도를 제안한다. 데이터 감축을 위한 속성의 중요도가 클러스터링의 결과에 지배 적이기 때문에 많은 속성의 변별력을 향상시키기 위하여 사후확률의 신뢰도에 정보 엔트로피를 적용하였다. 제안된 사후확률을 기반으로 한 러프 엔트로피 척도에 의한 속성의 신뢰도의 중복성은 엔트로피의 자연로그에 의하여 상당 히 줄어든다. 따라서 제안된 척도에 의하여 생성된 군집화 알고리즘은 속성값의 변별력을 향상시켜 기존의 리덕트를 최소화하였고, 이는 분할의 효율성을 향상시킬 수 있었다. 제안된 알고리즘의 검증을 위해 패턴분류 문제에 적용되는 ACME 데이터에 대하여 속성간의 변별력, 분할결과에 따른 분할의 순정도를 기존의 알고리즘과 비교 분석하였다.

In this paper, we propose a new measure based on the confidence of Bayesian posterior probability so as to reduce unimportant information in the clustering process. Because the performance of clustering is up to selecting the important degree of attributes within the databases, the concept of information entropy is added to posterior probability for attributes discernibility. Hence, The same value of attributes in the confidence of the proposed measure is considerably much less due to the natural logarithm. Therefore posterior probability-based clustering algorithm selects the minimum of attribute reducts and improves the efficiency of clustering. Analysis of the validation of the proposed algorithms compared with others shows their discernibility as well as ability of clustering to handle uncertainty with ACME categorical data.

보건의료데이터의 질적 수준을 향상시키기 위해서는 데이터 질 관리 알고리즘을 개발할 필요성이 있다. 이 에 본 연구에서는 질환의 유병률, 입원율이 높은 고혈압 환자의 당뇨질환 동반에 관련된 데이터 질 관리 알고리즘을 개발하고자 하였다. 이를 위해 2011년, 2012년 퇴원손상심층조사 자료 중 고혈압 환자 61,199건을 추출하여 분석대 상으로 하였다. 데이터 마이닝의 대화식 의사결정나무 방법과 Outlier Detection 방법론을 통해 데이터 질 관리 알고 리즘 개발한 결과 고혈압 환자가 당뇨병을 동반상병으로 가지는데 영향을 미치는 요인으로는 성별, 연령, 당뇨병성 사구체 장애, 당뇨병성 망막병증. 당병성 다발성 신경병증 등이 있었다. 의사결정나무 결과에 따라 당뇨병을 동반상 병으로 가질 확률 값이 80% 이상이거나, 20% 이하인 집단을 Outlier(극단치)로 정의하고, 고혈압 환자의 당뇨 동반 에 대한 극단치를 가지는 6개 집단을 발견하였다. 이와 같이 Outlier(극단치) 집단에 포함되는 실제 데이터를 확인하 여 데이터의 질적 수준을 향상 시킬 필요가 있다.

There is a need to develop a data quality management algorithm in order to improve the quality of health care data. In this study, we developed a data quality control algorithms associated diseases related to diabetes in patients with hypertension. To make a data quality algorithm, we extracted hypertension patients from 2011 and 2012 discharge damage survey data. As the result of developing Data quality management algorithm, significant factors in hypertension patients with diabetes are gender, age, Glomerular disorders in diabetes mellitus, Diabetic retinopathy, Diabetic polyneuropathy, Closed [percutaneous] [needle] biopsy of kidney. Depending on the decision tree results, we defined Outlier which was probability values associated with a patient having diabetes corporal with hypertension or more than 80%, or not more than 20%, and found six groups with extreme values for diabetes accompanying hypertension patients. Thus there is a need to check the actual data contained in the Outlier(extreme value) groups to improve the quality of the data.

본 논문는 ‘알고리즘’이라는 키워드를 중심으로 NAVER 뉴스 데이터에 텍스트 마이닝 기법을 적용한 것이다. 연구의 목적은 뉴스 데이터에서 ‘알고리즘’과 관련된 주요 트렌드와 주제를 식별하고 분석하는 것이었다. 이를 위해 데이터 수집, 전처리, 특성 벡터화, 토픽 모델링 등의 과정을 수행하였다. 데이터 수집은 NAVER API를 사용하여 ‘알고리즘’ 키워드가 포함된 뉴스 기사를 대상으로 하였다. 수집된 데이터는 텍스트 전처리를 거쳐 분석에 적합한 형태로 변환되었다. 이후 특성 벡터화를 통해 텍스트 데이터를 머신러닝 알고리즘이 처리할 수 있는 수치적 형태로 변환하였 으며, LDA와 LSA와 같은 토픽 모델링 기법을 적용하여 주요 토픽들을 추출하고 분석하였다. 분석 결과, ‘알고리즘’ 키워드는 다양한 뉴스 주제에 걸쳐 상당한 중요성을 지니고 있음이 밝혀졌다. 특히, 기술 발전, 사회적 영향, 비즈니 스 전략 등과 관련된 토픽들이 주요하게 다루어졌다. 본 연구는 알고리즘과 관련된 뉴스 콘텐츠의 트렌드와 주제를 파악하는 데 있어 텍스트 마이닝 기법의 유용성을 입증하였다.

This study applies text mining techniques to NAVER News data, focusing on the keyword 'algorithm'. The aim was to identify and analyze the major trends and topics related to 'algorithm' within the news data. The process involved data collection, preprocessing, feature vectorization, and topic modeling.Data collection was performed using the NAVER API, targeting news articles that contained the keyword 'algorithm'. The collected data underwent text preprocessing to transform it into a format suitable for analysis. Subsequently, feature vectorization converted the text data into a numerical form that machine learning algorithms can process. Topic modeling techniques, such as LDA and LSA, were applied to extract and analyze the main topics.The analysis revealed that the keyword 'algorithm' holds significant importance across various news topics. Notably, topics related to technological advancements, societal impact, and business strategies were predominantly featured. This research demonstrates the effectiveness of text mining techniques in identifying and understanding trends and topics associated with algorithms in news content.

With the continuous advancement of the data science and engineering, the combination of data analysis with other applications has been a trend. Data mining is in accordance with the established business goals from huge amounts of data to extract the effective potential and can be understood model of advanced treatment process. In this research paper, we conduct discussion on the execution of civil servants and professional ethics based on data analysis. Starting from the concept of data mining, this paper introduces the objects as the functions of data mining and mining process, mining algorithm combined with several kinds of common data mining, decision tree method, association rules method and neural network to its main ideas and improve the related description. We firstly review traditional data classification algorithms such as the Bayes classification algorithm to serve as the foundation. Later, we propose our method by using the modified SVM and deep neural network optimization. Then, we review the principles of civil servants and professional ethics with the combination of the prior discussion. The experimental result illustrates the feasibility and effectiveness of our method. We also discuss the future research plan in the final part.

일반적으로 최적의 교통신호를 생성하기 위해서는 교차로를 통행하는 평균차량 데이터를 미리 입력 시켜서 교통 신호주기를 생성 시키는 Time of Day(T.O.D.) 신호등 방식을 사용한다. 그리고 최적의 교통 신호를 예보하기 위해서 신경망 알고리즘 및 퍼지 알고리즘에 기반한 방법을 사용하였다. 그러나 신경망 알고리즘은 미래의 목표 값을 예측하는데 있어서 입력 벡터의 값의 수나 형태를 결정할 수 있는 체계적인 방법의 결여와 모델의 분류가 어떻게 이루어지는지 명확하게 이해 할 수 없는 단점이 있다. 본 논문에서는 이러한 단점을 해결하기 위해서 데이터 마이닝 기법인 C4.5 의사결정나무 알고리즘을 기존의 신경망 알고리즘에 결합시키는 방법을 제안한다. 실험결과 두 알고리즘을 결합할 때가 결합하지 않을 때 보다는 성능이 향상됨을 알 수 있었다. 또한 이 결합 알고리즘이 교통 예보능력이 우수함으로 실체 교통체계에 활용하기에 충분함을 알 수 있었다.

Generally, we use Time of Day(T.O.D.) method that makes traffic signal cycle with a average car data previously feeded to create a optimal traffic signal. And we used a traditional neural network or fuzzy algorithms to predict an optimal traffic signal until now. But the neural network algorithms have disadvantage, destitution of a systematic method and explicit model classification that can determine a number of a input vector value or a type. In this paper, we propose a combination method of a neural network and a data mining technology called C4.5 decision tree algorithm to solve this drawback. In experiments, the proposed method is more effective of performance than a traditional neural network algorithm method.

With the rapid development of big data, cloud computing, the size of the computer processing data is huge. Data mining is the process of revealing a new relationship, trend and pattern by a careful analysis of a large number of data. In this paper, the author analyzes data mining algorithm and the effectiveness of mathematics classroom teaching based on support vector machine. Through data analysis, the results show that teachers are more inclined to teach and ask questions, while students prefer to explore cooperative learning methods. In the process of classroom teaching, teachers should arouse students' enthusiasm and initiative, and further improve the efficiency of classroom teaching.

With the development of network and big data technology, how to dig out the effective information from massive data is the problem that we are facing. Traditional data mining algorithms in the face of big data, it is powerless. With the ability of parallel computing, data is processed on several machines, so the efficiency of data processing will be greatly improved. In this paper, the author analyzes the parallel data mining algorithm application in the evaluation of college counselors' psychological guidance ability, and puts forward the strategies of improving the ability of college counselors' psychological guidance. Based on the statistical analysis, the result shows that there were no significant differences in gender, but in the context of communication, female were better than male. The male instructors are superior to female instructors in the understanding ability of the observation and diagnosis and the plan of action. Obviously, training is a very effective way to improve the ability of College Counselors' psychological counseling, and it should be promoted and applied.

With the development of electronic commerce, the management mode of enterprises has been changed, E-business enterprises will also create new financial model, suitable for electronic commerce situation. In this paper, the author first put forward the data mining method based on SVM algorithm. Based on the empirical analysis, author research on the influence factors of e-commerce enterprise financial management mode, and makes a detailed analysis of the relationship between the environment, the network and the performance. For the electronic business enterprise, the solvency, development potential, innovation ability and adapt to the environment of the organization flexibility is the important index of E-enterprise performance evaluation index system.

Mobile Internet is a mainstream access and communication technology, due to access to Internet anytime and anywhere, the business will varied, and bring mass data, but the data processing has different characteristics, the delay and energy consumption are also different. Therefore, it is necessary to apply to different data mining methods in the cloud platform, so as to adapt to different business applications, this paper proposes an improved Apriori algorithm, theory and simulation can prove that the method is effective.

Mobile Internet is a mainstream access and communication technology, due to access to Internet anytime and anywhere, the business will varied, and bring mass data, but the data processing has different characteristics, the delay and energy consumption are also different. Therefore, it is necessary to apply to different data mining methods in the cloud platform, so as to adapt to different business applications, this paper proposes an improved Apriori algorithm, theory and simulation can prove that the method is effective.

The goal of data mining is to discover hidden useful information in large databases. Mining frequent patterns from transaction databases is an important problem in data mining. As the database size increases, the computation time and required memory also increase. Base on this, we use the MapReduce programming mode which has parallel processing ability to analysis the large-scale network. All the experiments were taken under hadoop, deployed on a cluster which consists of commodity servers. Through empirical evaluations in various simulation conditions, the proposed algorithms are shown to deliver excellent performance with respect to scalability and execution time.

Along with the rapid advancement of Internet technology and machine learning science, the data mining techniques have been widely applied on the web page information pattern analysis issues. To enhance the traditional mining algorithms theoretically and numerically, we propose the novel deep web data mining algorithm based on multi-agent information system and collaborative correlation rule in this manuscript. Firstly, we review the latest web mining methodologies to serve as the comparison objects. Then, we introduce the revised agent based algorithm. MAS consists of more than one agent, MAS using parallel distributed processing technology and modular design thought and the complex system is divided into relatively independent agent subsystem. Later, we combine the AdaBoost method to propose the collaborative correlation rule. As the combination, we use the mentioned two techniques to form the optimized and enhanced deep web data mining algorithm with the implementation of programming languages. The experimental result proves the feasibility of our approach and compared with other contemporary state-of-the-art algorithms, our method outperforms and achieves better accuracy with low time-consuming.

Data mining technology is a powerful tool to solve the problem. It is widely used to identify potentially useful information. Fuzzy principle based fuzzy C- means is a modification of commonly used C- means clustering technique. In the paper, a modified fuzzy C-means algorithm is used in the gene sequence. The modification is through taking a pseudo F statistics into the method. In the simulation, we use nodes instead of gene to verify the validity. According to the simulation, we get the optimal cluster number, the structure of the classification of the nodes. In order to test the performance of the algorithm, it has been used to process large amount of data, and results show that it has higher processing speed and stable performance. The algorithm can be used in the gene description.

Concerning the condition that there is a glittering array of disadvantages such as frequent candidate collection of Apriori algorithm, this paper comes up with cost-sensitive filtering matrix Apriori algorithm based on weighting. What’s more, with the help of FP-tree algorithm, we can carry out cost-sensitive learning through relevant data of its constructed decision tree to set different weighting for data and confidence level.