Analysis of Tourism Demand Difference Based on Data Mining and Intelligent Analysis

To serve as a reference for future foreign tourism study,relevant tourist sectors have done in-depth investigations on foreign tourism both domestically and internationally.A study of outbound tourism activities from the viewpoint of tourists can examine its development law and create successful marketing tactics based on the rise in the number of foreign tourists.Based on this,this study suggests a data mining technique to examine the variations in travel needs and marketing tactics among various consumer groups.The combined example analysis demonstrates how logical and useful our data mining analysis is.Our data tests demonstrate that the tourism strategy outlined in this paper can enhance the number of tourists by piquing their interest based on the rise in the number of international travellers travelling overseas.

Data mining-based analysis of acupoint selection patterns for chronic hepatitis B infection

Background:The purpose of this study was to identify the characteristics and principles of acupoints applied for treating chronic hepatitis B infection.Methods:The published clinical studies on acupuncture for the treatment of chronic hepatitis B infection were gathered from various databases,including SinoMed,Chongqing Vip,China National Knowledge Infrastructure,Wanfang,the Cochrane Library,PubMed,Web of Science and Embase.Excel 2019 was utilized to establish a database of acupuncture prescriptions and conduct statistics on the frequency,meridian application,distribution and specific points,as well as SPSS Modeler 18.0 and SPSS Statistics 26.0 to conduct association rule analysis and cluster analysis to investigate the characteristics and patterns of acupoint selection.Results:A total of 42 studies containing 47 acupoints were included,with a total frequency of 286 acupoints.The top five acupoints used were Zusanli(ST36),Ganshu(BL18),Yanglingquan(GB34),Sanyinjiao(SP6)and Taichong(LR3),and the most commonly used meridians was the Bladder Meridian of Foot-Taiyang.The majority of acupuncture points are located in the lower limbs,back,and lumbar regions,with a significant percentage of them being Five-Shu acupoints.The strongest acupoint combination identified was Zusanli(ST36)–Ganshu(BL18),in addition to which 13 association rules and 4 valid clusters were obtained.Conclusion:Zusanli(ST36)–Ganshu(BL18)could be considered a relatively reasonable prescription for treating chronic hepatitis B infection in clinical practice.However,further high-quality studies are needed.

Development of the Data Processing and Analysis System Framework for ICF Experiments

An idea is presented about the development of a data processing and analysis system for ICF experiments, which is based on an object oriented framework. The design and preliminary implementation of the data processing and analysis framework based on the ROOT system have been completed. Software for unfolding soft X-ray spectra has been developed to test the functions of this framework.

Study on Factors Affecting Springback and Application of Data Mining in Springback Analysis

Springback of sheet metal induced by elastic recovery is one of major defects in sheet metal forming processed. Springback is influenced by many factors including properties of the sheet material and processing conditions. In this paper, a springback simulation was conducted and comparisons between the results based on different processing variables were illustrated. The discovery of knowledge of the effects of geometry and process parameters on springback from FEM results becomes increasingly important, as the number of numerical simulation has grown exponentially. Data mining is an effective tool to realize knowledge discovery in simulation results. A data-mining algorithm, rough sets theory (RST), was applied to analyze the effects of process parameters on springback in U-bending.

Correlation knowledge extraction based on data mining for distribution network planning

Traditional distribution network planning relies on the professional knowledge of planners,especially when analyzing the correlations between the problems existing in the network and the crucial influencing factors.The inherent laws reflected by the historical data of the distribution network are ignored,which affects the objectivity of the planning scheme.In this study,to improve the efficiency and accuracy of distribution network planning,the characteristics of distribution network data were extracted using a data-mining technique,and correlation knowledge of existing problems in the network was obtained.A data-mining model based on correlation rules was established.The inputs of the model were the electrical characteristic indices screened using the gray correlation method.The Apriori algorithm was used to extract correlation knowledge from the operational data of the distribution network and obtain strong correlation rules.Degree of promotion and chi-square tests were used to verify the rationality of the strong correlation rules of the model output.In this study,the correlation relationship between heavy load or overload problems of distribution network feeders in different regions and related characteristic indices was determined,and the confidence of the correlation rules was obtained.These results can provide an effective basis for the formulation of a distribution network planning scheme.

Research of intelligence data mining based on commanding decision-making

In order to rapidly and effectively meet the informative demand from commanding decision-making, it is important to build, maintain and mine the intelligence database. The type, structure and maintenance of military intelligence database are discussed. On this condition, a new data-mining arithmetic based on relation intelligence database is presented according to the preference information and the requirement of time limit given by the commander. Furthermore, a simple calculative example is presented to prove the arithmetic with better maneuverability. Lastly, the problem of how to process the intelligence data mined from the intelligence database is discussed.

Image Post-Processing Method for Visual Data Mining

Visual data mining is one of important approach of data mining techniques. Most of them are based on computer graphic techniques but few of them exploit image-processing techniques. This paper proposes an image processing method, named RNAM （resemble neighborhood averaging method）, to facilitate visual data mining, which is used to post-process the data mining result-image and help users to discover significant features and useful patterns effectively. The experiments show that the method is intuitive, easily-understanding and effectiveness. It provides a new approach for visual data mining.

Research on the rules of Professor Dai Xiaohua's medication for coronary heart disease and chronic heart failure based on data mining

Objective: To analyze and discuss the rules and thoughts of Professor Dai Xiaohua's treatment of coronary heart disease and chronic heart failure using data mining technology. Methods: The medical records of outpatients from Professor Dai Xiaohua, who were treated in the Department of Cardiology, First Affiliated Hospital of Anhui University of Traditional Chinese Medicine from June 2019 to October 2019 and standardize it. With the help of Microsoft Excel 2016, SPSS 23.0, and SPSS Modeler 18.0 computer software, a database was established and the frequency of medication, association rules, and cluster analysis were performed. Results: Of the 109 prescriptions included in the study, 87 Chinese medicines were involved. The top 10 drugs are Chenpi, Atractylodes, Dandelion, Zhuru, Muxiang, Fructus aurantii, Ophiopogon, Poria, Sour Jujube, semen coicis. A total of 10 associations such as "Bamboo Ru → Dandelion, Dandelion → Cinnamon";Cluster analysis yields 5 clustering square groups. Conclusion: Professor Dai Xiaohua's treatment of coronary heart disease and chronic heart failure focuses on regulating qi and strengthening spleen, resolving phlegm and dampness.

Data Mining and Machine Learning Methods Applied to A Numerical Clinching Model

Numerical mechanical models used for design of structures and processes are very complex and high-dimensionally parametrised.The understanding of the model characteristics is of interest for engineering tasks and subsequently for an efficient design.Multiple analysis methods are known and available to gain insight into existing models.In this contribution,selected methods from various fields are applied to a real world mechanical engineering example of a currently developed clinching process.The selection of introduced methods comprises techniques of machine learning and data mining,in which the utilization is aiming at a decreased numerical effort.The methods of choice are basically discussed and references are given as well as challenges in the context of meta-modelling and sensitivities are shown.An incremental knowledge gain is provided by a step-bystep application of the numerical methods,whereas resulting consequences for further applications are highlighted.Furthermore,a visualisation method aiming at an easy design guideline is proposed.These visual decision maps incorporate the uncertainty coming from the reduction of dimensionality and can be applied in early stage of design.

Research spot and trend of integrative nursing in breast diseases based on data mining

Objective:To explore the development status,research hotspots and future development trend of traditional Chinese medicine nursing in the field of breast diseases by the bibliometric method.Methods:using CNKI,Wanfang,VIP and SINOMED databases as data sources,the literature related to breast traditional Chinese medicine nursing were obtained by keyword search,and BICOMB 2.01 was used to extract and count the information such as publication year,keywords and so on,and the word matrix was formed.SPSS 21.0 was used to cluster the extracted keywords.Results:a total of 839 articles were included,which came from 348 journals and 48 high-frequency words.Four main research topics were formed by keyword clustering:TCM nursing of breast cancer,TCM nursing of breast abscess/mass,TCM nursing of postpartum breast,TCM nursing of acute mastitis.Conclusion:the TCM nursing literature in the field of breast disease is increasing rapidly,but theoverall quality of the published literature is low,so more scientific and standardized TCM nursing original research is urgently needed.

Data Mining for Flooding Episode in the States of Alagoas and Pernambuco—Brazil

The increasing volume of data in the area of environmental sciences needs analysis and interpretation. Among the challenges generated by this “data deluge”, the development of efficient strategies for the knowledge discovery is an important issue. Here, statistical and tools from computational intelligence are applied to analyze large data sets from meteorology and climate sciences. Our approach allows a geographical mapping of the statistical property to be easily interpreted by meteorologists. Our data analysis comprises two main steps of knowledge extraction, applied successively in order to reduce the complexity from the original data set. The goal is to identify a much smaller subset of climatic variables that might still be able to describe or even predict the probability of occurrence of an extreme event. The first step applies a class comparison technique: p-value estimation. The second step consists of a decision tree (DT) configured from the data available and the p-value analysis. The DT is used as a predictive model, identifying the most statistically significant climate variables of the precipitation intensity. The methodology is employed to the study the climatic causes of an extreme precipitation events occurred in Alagoas and Pernambuco States (Brazil) at June/2010.

Medical Data Mining: the pilot of medical data analysis in the era of big data

Clinical databases have accumulated large quantities of information about patients and their medical conditions. Current challenges in biomedical research and clinical practice include information overload and the need to optimize workflows, processes and guidelines, to increase capacity while reducing costs and improving efficiency. There is an urgent need for integrative and interactive machine learning solutions, because no medical doctor or biomedical researcher can keep pace today with the increasingly large and complex data sets – often called "Big Data".

Research on Rolling Load Distribution Method based on Data Mining

A new method of establishing rolling load distribution model was developed by online intelligent information-processing technology for plate rolling. The model combines knowledge model and mathematical model with using knowledge discovery in database （KDD） and data mining （DM） as the start. The online maintenance and optimization of the load model are realized. The effectiveness of this new method was testified by offline simulation and online application.

抢先赢得商机的Data Mining──基于数据仓库的数据挖掘技术

首先介绍了数据仓库以及在此技术上产生的数据挖掘技术,其次阐述了实现数据挖掘应用的几种工具以及选用工具时应遵循的原则,最后说明了数据挖掘技术现存的问题及他现在重要的商业地位。

Discussion on the law of acupoint selection by acupuncture in the treatment of post-stroke depression based on the data mining technique

Objective:To analyze the law of acupoint selection in treatment of depression after stroke by using data mining technique,providing reference for clinical treatment.Methods:Through the retrieval of CNKI,wanfang data resource system and Vip Database,relevant literature on acupuncture treatment of this disease in China from the inception of the database to 2019 were collected.Thus the database of acupoint prescriptions was established.SPSS20.0 and SPSS Modeler 18.0 software were used for statistical analysis to explore the law of acupoint selection.Results:A total of 92 articles were selected,92 acupuncture prescriptions and 88 acupoints with a total frequency of 715 times.The frequency of acupoints of Du channel ranked first.The frequency of acupoints in the head and neck and lower limbs were successively 40.42%(289/715)and 20.46%(172/715).Specific acupoints accounted for 81.82%of the acupoints used,among which confluent acupoints ranked first.The correlation analysis results showed that the highest support of acupoint combination was Baihui(GV20)and Shenting(GV24).Conclusion:The acupoint selection in the treatment of PSD gives priority to the Du channel,following the principle of local acupoint selection,local and remote acupoint selection with the precondition of regulation the mind.Attach importance to the use of specific acupoints,especially the confluent points.

Study on the medication rules of traditional Chinese medicine in the treatment of sleep disorder after stroke based on data mining

Objective:To explore the medication rule of Traditional Chinese Medicine(TCM)in the treatment of sleep disorder after stroke by using data mining technology.Methods:A computer search was used to search the electronic database of clinical literature on the treatment of sleep disorders after stroke by TCM from January 2000 to January 2021.Excel was used to establish the database,and the prescription information was described and analyzed statistically.Using IBM SPSS Modeler 18.0 software,Apriori algorithm was used for TCM association analysis,and IBM SPSS 22.0 software was used for systematic cluster analysis of high-frequency TCM.Results:A total of 67 literatures were included,covering 131 traditional Chinese medicines.The medecines with a higher frequency of sodium use include Ziziphi Spinosae Semen(Suanzaoren),Angelicae Sinensis Radix(Danggui),Ligusticum(Chuanxiong),liquorice(Gancao),Poria cocos(Fuling),and so on.From the effect point of view,deficiency-tonifying medicine,sedative medicine and blood-activating and stasis-removing medicine are commonly used.The medicinal properties are mainly cold,mild and warm.The main medicine flavor are sweet and bitter.The medicines mostly belong to the liver,heart and spleen Meridian.Thirty-three association rules were obtained for medicine pairs and medicine groups from the correlation analysis,and the core combinations were"Ziziphi Spinosae Semen(Suanzaoren)-Tuber fleeceflower stem(Yejiaoteng)","Ziziphi Spinosae Semen(Suanzaoren)-Polygala(Yuanzhi)","Ziziphi Spinosae Semen(Suanzaoren)-Cortex albiziae(Hehuanpi)"and"Angelicae Sinensis Radix(Danggui)-Radix bupleuri(Chaihu)-Radix Paeoniae Alba(Baishao)"and so on.Seven medicine aggregation groups were obtained by medicine cluster analysis.Conclusion:In the treatment of sleep disorder after stroke by TCM,the main method is to calm the heart and mind.Meanwhile,according to different syndrome types,the treatment methods of tonifying the heart and spleen,nourishing the liver and kidney,soothing the liver and softening the liver,clearing heat and resolving phlegm,nourishing the blood and promoting blood circulation are selected,which provide certain reference for clinical treatment.

A study on the rule of Chinese medicine use for airway remodeling based on data mining

Objective:Use data mining techniques to explore the rule of Chinese medicine used for airway remodeling.Methods:Search the literature on Chinese medicine use for airway remodeling in the past 20 years.With the help of WPS Office Excel 11.1,IBM SPSS Statistics 23.0 and SPSS Modeler 18.0 software,prescriptions were analyzed for the frequency of drug use,the four natures,the five flavours and the channel tropism,cluster analysis and association analysis of high-frequency drugs.Results:There were 58 Chinese medicine prescriptions for airway remodeling be found,involving 105 Chinese medicines,the most frequent channel tropism were spleen,stomach,lung,large intestine,liver and gallbladder,the most frequent use of the five flavors was sour,sweet and pungent,the highest frequency of the four natures was cold and hot,cluster analysis yielded eight drug aggregation groups,and association rule analysis yielded five groups of high-frequency drug pairs.Conclusion:The main TCM treatments for airway remodeling are expelling phlegm,relieving cough,asthma calming,expelling blood stasis and deficiency tonifying.The results of this study can provide ideas for compounding and drug selection for subsequent studies.