A Short Review of Classification Algorithms Accuracy for Data Prediction in Data Mining Applications

Many business applications rely on their historical data to predict their business future. The marketing products process is one of the core processes for the business. Customer needs give a useful piece of information that helps to market the appropriate products at the appropriate time. Moreover, services are considered recently as products. The development of education and health services is depending on historical data. For the more, reducing online social media networks problems and crimes need a significant source of information. Data analysts need to use an efficient classification algorithm to predict the future of such businesses. However, dealing with a huge quantity of data requires great time to process. Data mining involves many useful techniques that are used to predict statistical data in a variety of business applications. The classification technique is one of the most widely used with a variety of algorithms. In this paper, various classification algorithms are revised in terms of accuracy in different areas of data mining applications. A comprehensive analysis is made after delegated reading of 20 papers in the literature. This paper aims to help data analysts to choose the most suitable classification algorithm for different business applications including business in general, online social media networks, agriculture, health, and education. Results show FFBPN is the most accurate algorithm in the business domain. The Random Forest algorithm is the most accurate in classifying online social networks (OSN) activities. Naïve Bayes algorithm is the most accurate to classify agriculture datasets. OneR is the most accurate algorithm to classify instances within the health domain. The C4.5 Decision Tree algorithm is the most accurate to classify students’ records to predict degree completion time.

On Application of Big Data Mining in Earthquake Precursor Observation

Research and application of big data mining,at present,is a hot issue. This paper briefly introduces the basic ideas of big data research, analyses the necessity of big data application in earthquake precursor observation,and probes certain issues and solutions when applying this technology to work in the seismic-related domain. By doing so,we hope it can promote the innovative use of big data in earthquake precursor observation data analysis.

Data Mining Algorithm Implementation and Its Application in Parallel Cloud System based on C＋＋

. This paper conducts the analysis on the data mining algorithm implementation and its application in parallel cloud system based on C＋＋. With the increase in the number of the cloud computing platform developers, with the use of cloud computing platform to support the growth of the number of Internet users, the system is also the proportion of log data growth. At present applies in the colony environment many is the news transmission model. In takes in the rest transmission model, between each concurrent execution part exchanges the information, and the coordinated step and the control execution through the transmission news. As for the C＋＋ in the data mining applications, it should ？ rstly hold the following features. Parallel communication and serial communication are two basic ways of general communication. Under this basis, this paper proposes the novel perspective on the data mining algorithm implementation and its application in parallel cloud system based on C＋＋. The later research will be focused on the code based implementation.

The analysis of application of data mining technology in the system of intrusion detection

With the economic development and the popularity of application of electronic computer, electronic commerce has rapid development. More and more commerce and key business has been carried on the lnternet because Internet has the features of interaction, openness, sharing and so on. However, during the daily commerce, people worry about the security of the network system. So a new technology which can detect the unusual behavior in time has been invented in order to protect the security of network system. The system of intrusion detection needs a lot of new technology to protect the data of the network system. The application of data mining technology in the system of intrusion detection can provide a better assistant to the users to analyze the data and improve the accuracy of the checking system.

Research on the Structured Data Mining Algorithm and the Applications on Machine Learning Field

In this paper, we conduct research on the structured data mining algorithm and applications on machine learning field. Various fields due to the advancement of informatization and digitization, a lot of multi-source and heterogeneous data distributed storage, in order to achieve the sharing, we must solve from the storage management to the interoperability of a series of mechanism, the method and implementation technology. Unstructured data does not have strict structure, therefore, compared with structured information that is more difficult to standardization, with management more difficult. According to these characteristics, the large capacity of unstructured data or using files separately store, is stored in the database index of similar pointer. Under this background, we propose the new idea on the structured data mining algorithm that is meaningful.

Analysis on The Application of Data Mining Technology in Computer Network Virus Defense

With the rapid development of computer network,the society has entered the information and digital era,it plays an important role in speeding up the pace of social development and providing more convenient services for people.However, the security problem of computer network is becoming more and more serious. All kinds of network viruses pose a great threat to the security of computer network.As the most advanced data processing technology currently, data mining technology can effectively resist the invasion of network virus to computer system,and plays an important role in improving the security of the computer network.This paper starts with the concept of data mining technology and the characteristics of computer network virus,and makes an in-depth analysis on the specific application of data mining technology in the computer network virus defense.

Research on the application of the data mining technology in the electronic commerce network marketing

With the rapid development of China＇s reform and opening up and the socialist market economy, the development of Internet technology has promoted the prosperity of e-commerce, and further promoted the rapid development of China＇s economy. Data mining technology is an advanced science and technology, which has important implications for the e-commerce data processing. Through the summary of the data mining technology, this article puts forward the application of data mining technology in electronic commerce, in order to better promote the development of electronic commerce.

Application of Data mining in Enterprise Network Marketing

With the rapid development of the Internet, market has been increasingly competitive and competition means are various. Internet Marketing has become a new way for enterprises to grow. Data mining of enterprise network marketing has become the new darling of many business managers.The marketing data will become a key to develop a corporate marketing strategy as an important basis tool. However, many companies now make mistakes in marketing data. They can＇t fully exploit the marketing data.lt can affect the development of enterprise network marketing strategy. This paper is based on the concept of marketing data and outline the importance of data mining for network marketing, then analyze a significant impact on the entemrise network marketin~ strate^w made by marketinR data mininR.

URL Phishing Detection Using Particle Swarm Optimization and Data Mining

The continuous destruction and frauds prevailing due to phishing URLs make it an indispensable area for research.Various techniques are adopted in the detection process,including neural networks,machine learning,or hybrid techniques.A novel detection model is proposed that uses data mining with the Particle Swarm Optimization technique(PSO)to increase and empower the method of detecting phishing URLs.Feature selection based on various techniques to identify the phishing candidates from the URL is conducted.In this approach,the features mined from the URL are extracted using data mining rules.The features are selected on the basis of URL structure.The classification of these features identified by the data mining rules is done using PSO techniques.The selection of features with PSO optimization makes it possible to identify phishing URLs.Using a large number of rule identifiers,the true positive rate for the identification of phishing URLs is maximized in this approach.The experiments show that feature selection using data mining and particle swarm optimization helps tremendously identify the phishing URLs based on the structure of the URL itself.Moreover,it can minimize processing time for identifying the phishing website instead.So,the approach can be beneficial to identify suchURLs over the existing contemporary detecting models proposed before.

Research and Application Progress of Agricultural Big Data Technology and Resources

With the development of Internet of things, cloud computing, mobile Inter- net, the scale of the data shows an alarming growth trend. Agricultural information is an important part of modern agricultural construction, and the development of a- gricultural industry is becoming more and more deeply with the application of infor- mation technology. This paper reviewed the concept and characteristic of big data, development history of big data at home and abroad, and emphatically expounded the connotation of agricultural big data, development status of agricultural big data at home and abroad, as well as the applications of agricultural big data technology, agriculture big data resources and agricultural big data in various fields.

Data mining techniques在冶金领域的应用

介绍了Data mining techniques产生背景、发展情况、优化过程,以及在冶金领域的应用。

数据挖掘系统开发及Oracle Data Mining应用

数据挖掘技术目前主要应用于智能商务领域,在石化领域中如何展开数据挖掘开发应用还有待研究。基于Oracle Data Mining(ODM)数据挖掘工具。

Using Data Mining with Time Series Data in Short-Term Stocks Prediction: A Literature Review

Data Mining (DM) methods are being increasingly used in prediction with time series data, in addition to traditional statistical approaches. This paper presents a literature review of the use of DM with time series data, focusing on shorttime stocks prediction. This is an area that has been attracting a great deal of attention from researchers in the field. The main contribution of this paper is to provide an outline of the use of DM with time series data, using mainly examples related with short-term stocks prediction. This is important to a better understanding of the field. Some of the main trends and open issues will also be introduced.

How to Apply Data Mining Technology to the Study of Agricultural Information Data Resources?

This paper makes a brief description of the definition and methods of data mining.It describes the characteristics of agricultural data(value delivery,specialization,spatio-temporal bidimensionality)and the status of application of data mining technology in agriculture.

Remote sensing for agricultural applications

Agricultural geospatial information is critical for agricultural policy formulation and decision making, land use monitoring, agricultural sustainability, crop acreage and yield estimation, disaster assessment, bioenergy crop inventory, food security policy, environmental assessment, carbon accounting, and other research topics that are of vital importance to agricul- ture and economy. Remote sensing technology enables us to collect, process, and analyze remotely sensed data and to retrieve, synthesize, visualize valuable geospatial information for agriculture uses. Specifically, remote sensing technology empowers capability for large scale field level or regional assessment and monitoring of crop land cover,

Research on laws of effect of different acupuncture-moxibustion therapies based on data mining

Different acupuncture-moxibustion therapies can produce different clinical effects, that is, the effect has specificity, which is significantly important in obtaining acupuncture-moxibustion efficacy. In this study, the clinical application laws of fire needle, acupoint injection, catgut embedment, acupoint application, moxibustion therapy and filiform needle acupuncture were summarized in the aspects of category of disease, efficacy and related prescriptions （such as medication and acupoint selection） based on the result of data mining, and the general applicable categories of disease of acupuncture-moxibustion treatment methods were further screened, so as to guide the clinical application and give play to the best efficacy.

Extending JASAG with data processing techniques for speeding up agricultural simulation applications:A case study with Simugan

Resource-intensive agricultural simulation applications have increased the need for gridification tools–i.e.,software to transform and scale up the applications using Grid infrastructures–.Previous research has proposed JASAG,a generic gridification tool for agricultural applications,through which the performance of a whole-farm simulation application called Simugan improved considerably.However,JASAG still lacks proper support for efficiently exploiting Grid storage resources,causing significant delays for assembling and summarizing the generated data.In this application note,two different data processing techniques in the context of JASAG are presented to tackle this problem.Simugan was again employed to validate the benefits of these techniques.Experiments using data processing techniques show that the execution time of Simugan was accelerated by a factor of up to 34.34.

Acupoint combinations used for treatment of Alzheimer's disease: A data mining analysis

OBJECTIVE: To identify the acupoint combinations used in the treatment of Alzheimer's disease(AD).METHODS: The clinical literature regarding acupuncture and moxibustion for AD was searched and collected from databases including Chinese Biomedical Medicine, China National Knowledge Infrastructure, Wanfang Database and PubMed. The database of acupuncture and moxibustion prescriptions for AD was established by using Excel software so as to conduct the descriptive analysis, association analysis on the data.RESULTS: Baihui(GV 20), Sishencong(EX-HN 1),Shenmen(HT 7), Zusanli(ST 36), Neiguan(PC 6),Fengchi(GB 20), Taixi(KI 3), Dazhui(GV 14), Shenshu(BL 23), Sanyinjiao(SP 6), Shenting(GV 24), Fenglong(ST 40), Xuanzhong(GB 39), Shuigou(GV 26)and Taichong(LR 3) were of higher frequency in the treatment of AD with acupnucture and moxibustion. Most acupoints were selected from the Governor Vessel. The commonly used acupoints were located on the head, face, neck and lower limbs. The combination of the local acupoints with the distal ones was predominated. The crossing points among the specific points presented the advantage in the treatment. The association analysis indicated that the correlation among Fengchi(GB 20)-Baihui(GV 20) was the strongest, followed by combinations of Dazhui(GV 14)-Baihui(GV 20), Shenshu(BL 23)-Baihui(GV 20) and Neiguan(PC 6)-Baihui(GV 20) and indicated the common rules of the clinical acupoint selection and combination for AD.CONCLUSION: Our findings provide a reference for acupoints selection and combination for AD in clinical acupuncture practice.

Agricultural Product Quality Mining Based on Bayesian Classification

Combined with the characteristics of crop growth and environmental data and the basic principle of Bayesian algorithm,the crop product quality is analyzed and forecasted in this study.Test with a randomly selected sample group ensures high forecasting accuracy,which shows that the algorithm is effective.

大数据时代的农业发展:国际前沿与中国实践

随着信息革命、数据革命的到来,农业生产已经发生了翻天覆地的变化。在新时代,传统农业逐步遇到瓶颈,亟待向着精准农业、智慧农业等新型数字农业发展模式转型。21世纪以来,世界多国探索了农业大数据的十余个领域,发展了一系列数据收集、存储、分析的技术和方法,并取得了显著的应用成效。中国面临着人口多、耕地少等一系列问题,应紧跟技术进步,结合中国国情和农业特征,加快发展农业新质生产力,在农业大数据的基础研究、行业标准、基础设施、供应链管理、金融保险等方面着力,促进农业高质量发展,加快建设农业强国,促进农业农村现代化。