In our daily study, work and life, everyone will come into contact with papers to some extent. The paper is of great significance to all educators and the improvement of human understanding. Do you know how to write a good paper? The following is my collection of papers on Web data mining technology for your reference, hoping to help friends in need.

Brief Introduction to Web Data Mining Technology Papers 1

At present, with the rapid development of network technology and database technology, it has effectively promoted the transformation of business activities from traditional activities to e-commerce. E-commerce is to realize the electronization, digitalization and networking of the whole business activities by using computer and network technology and remote communication technology. With the rapid development of e-commerce based on the Internet, modern enterprises have accumulated a large amount of data, which can not only bring more useful information to enterprises, but also enable other modern enterprise managers to collect a large amount of data in time and accurately. Visiting customers to provide more and better services has become a key factor in the success or failure of e-commerce, so it has been highly concerned by modern e-commerce operators, which also puts forward new requirements for computer web data technology, and Web data mining technology has emerged. It is a brand-new technology, which can obtain a large amount of data from the Internet and effectively extract useful information for enterprise decision makers to analyze and reference, so as to scientifically and reasonably formulate and adjust marketing strategies and provide dynamic, personalized and efficient services for customers. At present, it has become an indispensable carrier in e-commerce activities.

Summary of computer web data mining

1. the origin of computer web data mining

Computer Web data mining is a process of filtering useful data information from Web resources. Web data mining is to transplant the traditional ideas and methods of data mining into Web applications, that is, to select interesting and useful patterns or hidden data information from existing Web documents and activities. Computer Web data mining can show its role in many fields. It has been widely used in database technology, information acquisition technology, statistics, machine learning in artificial intelligence and neural networks, among which the most obvious is that it has played a huge role in promoting the reform of business activities.

2. The meaning and characteristics of computer Web data mining.

The Significance of (1)Web Data Mining

Web data mining refers to the application of data mining technology in Web environment. It is a new technology combining data mining technology with WWW technology, and has been widely used in computer language, Internet, artificial intelligence, statistics, informatics and other fields. Specifically, it is to make full use of the Internet to find hidden, potentially useful and valuable information from log files, commodity information, search information, purchase and sale information and network user registration information, and then use it for enterprise management and business decision-making.

(2) Characteristics of 2)Web data mining

Computer Web data mining technology has the following characteristics: first, users do not need to provide subjective evaluation information; Second, the user's "dynamic acquisition mode acquisition" will not be outdated; Third, it can handle large-scale data and is convenient to use; Fourthly, compared with traditional databases and data warehouses, the Web is a huge, widely distributed and global information service center.

(3) Classification of computer web data mining technology

There are three Web data mining technologies: the first is web usage record mining. It discovers information such as users' access patterns to Web pages and potential customers by mining web logs through the network, so as to improve the competitiveness of all services on its website. The second category is Web content mining. It refers to the process of extracting knowledge from Web documents. The third category is Web structure mining. By summarizing, clustering and analyzing the contents of a large number of documents on the Web, we can predict relevant information and knowledge from the organizational structure and link relationship of Web documents.

The relationship between computer web data mining technology and e-commerce

With the maturity of computer technology and network technology, e-commerce has been paid more and more attention by enterprises and individuals because of its fast and convenient characteristics. With the continuous expansion of the business scale of e-commerce enterprises, the number of goods and customers of e-commerce enterprises has also increased rapidly, and e-commerce enterprises have obtained a large number of data, which are becoming important information for customer management and sales management of e-commerce enterprises. In order to better develop and utilize these data resources and bring more convenience and benefits to enterprises and customers, various data mining technologies are gradually applied to e-commerce websites. At present, e-commerce recommendation system based on data mining (especially web data mining) technology is becoming a trend of e-commerce recommendation system development.

The concrete application of computer web data mining in e-commerce

(1) web data mining process in e-commerce

In e-commerce, the process of web data mining mainly has the following three stages: data preparation stage, data mining operation stage and result expression and interpretation stage. If the analysis results can't satisfy the decision-makers of e-commerce enterprises in the result expression stage, we must repeat the above process until we are satisfied.

(2) The application of 2)Web data mining technology in e-commerce.

At present, the wide application of e-commerce in enterprises has greatly promoted the rise of e-commerce websites. By analyzing the user's visit information to the website in a certain period of time, we can find the potential customer groups, related pages, clustered customers and other data information on the business website, and the enterprise information system will get a lot of data. With so much data, Web data mining has a rich data base, which makes it more important and practical in various commercial fields. Therefore, e-commerce will be the main direction of Web data mining in the future. The application of Web data mining technology in e-commerce mainly includes the following aspects:

The first is to find potential customers. In e-commerce activities, vendors of enterprises can use classification technology to find potential customers on the Internet, classify visitors by mining information resources such as Web logs, find the characteristics and laws of visiting customers, and then find potential customers from the existing classification.

The second is to retain visiting customers. E-commerce enterprises can fully tap the information left by customers in the process of browsing and visiting through business websites, understand their browsing behavior, and then make satisfactory page recommendations and specialized products in time according to the different hobbies and requirements of visiting customers, so as to continuously improve the satisfaction of website visits, maximize the stay time of customers, and achieve the purpose of retaining old customers and exploring new customers.

The third is to provide marketing strategy reference. Through Web data mining, manufacturers of e-commerce enterprises can explore the channels and sales of goods. At the same time, combined with the changes of the market, through cluster analysis, the laws of customer visits, different consumption needs and the life cycle of consumer goods are deduced, which provides timely and accurate information reference for decision-making, so that decision-makers can adjust commodity sales strategies in time and optimize commodity marketing.

The fourth is to improve the design of business websites. Designers of e-commerce websites can use association rules to understand customer behavior records and feedback, and improve the website on this basis, constantly optimize the organizational structure of the website to facilitate customers' access and continuously improve the click-through rate of the website.

label

This paper summarizes the Web data mining technology, and expounds its wide application in e-commerce. It can be seen that with the rapid development of computer technology and database technology, the application of computer Web data technology will be more extensive, and Web data mining will also become a very important research field with great research prospects and far-reaching significance. At present, the application of Web data in China is still in its infancy, and there are still many problems worthy of in-depth study.

Analysis of WEB data mining technology Paper 2 Abstract: This paper introduces the basic knowledge of e-commerce and data mining, and analyzes the application of Web data mining technology in e-commerce from several aspects.

Keywords: e-commerce; Data mining; App application

1 overview

E-commerce refers to the activities of enterprises or individuals to exchange business data and develop business services by using modern information technology based on the network and adopting electronic means. With the rapid development of Internet, e-commerce has more obvious advantages than traditional commerce. E-commerce has gradually become an indispensable activity in people's lives because of its convenience, flexibility and quickness. At present, there are many e-commerce platform websites, and the industry competition is fierce. In order to obtain more customer resources, e-commerce websites must strengthen customer relationship management, improve business philosophy and enhance after-sales service. Data mining is a process of identifying hidden, potentially useful, effective, novel and understandable information and knowledge from data sets. Inductive reasoning, mining and business forecasting from data sets can help decision makers of e-commerce enterprises adjust their market strategies according to forecasts, thus reducing enterprise risks, making correct decisions and maximizing profits. With the increasing application of e-commerce, a large number of useful data will be produced in e-commerce activities. How can we dig out the reference value of data? Study customers' hobbies, classify customers and recommend their favorite products to relevant customers respectively. Therefore, how to mine data on e-commerce platform has become a hot issue.

2 Overview of data mining technology

Data mining, also known as knowledge discovery database (KDD). Data mining generally refers to the process of applying algorithms to discover hidden unknown information from massive data. Data mining is a process of discovering the relationship between models and data by using analytical tools in big data resources. Data mining plays a key role for decision makers to discover some potential relationships between data and find hidden factors. These models have potential value and are understandable. Data mining is a multidisciplinary knowledge, which combines the theories and technologies of artificial intelligence, machine learning, database, statistics, visualization, information retrieval, parallel computing and other fields. These subjects also provide strong technical support for data mining.

3Web data mining features

Web data mining is the application of data mining on the Web. The purpose of Web data mining is to find valuable data or information from the content of web pages, hyperlink structures and the usage logs of the World Wide Web. According to the data categories used in the mining process, Web data mining tasks can be divided into: Web content mining, Web structure mining and Web usage record mining.

1) Web content mining refers to extracting text, pictures or other information that constitutes Web content. The objects of mining usually include text, graphics, audio and video, multimedia and other types of data.

2) Web page structure mining is to mine the structure between web pages, describe how the content is organized, and find the page structure and valuable patterns in the page structure from the hyperlink structure of web pages. For example, find out which important web pages are from these links, automatically cluster and classify them according to topics, and obtain useful information from web pages according to patterns for different purposes, thus improving the quality and efficiency of retrieval.

3)Web usage record mining is a method based on users' access records to the server. Web uses mining to map log data to relational tables, and uses corresponding data mining technology to access log data, and discovers user navigation behavior by collecting and analyzing user click events. It is used to extract information about how customers browse and use links to access web pages. Such as which pages have you visited? How long do you stay on each page? What did you click next? On what route did you quit browsing? These are the problems to be solved by Web usage record mining.

Application Analysis of Web Mining Technology in E-commerce

Application of 1) sequence pattern analysis in e-commerce

Sequential pattern data mining is to mine patterns based on time or other sequences. For example, in a set of dialogues or transactions arranged in chronological order, one item exists after another. In this way, the network provider can predict the future access mode to help set up the advertisement transmission for a specific user group. It is found that the sequential pattern is easy to make consumers' behavior predicted by the organizers of e-commerce. When users browse the website, they should cater to each user's browsing habits as much as possible, and constantly adjust the webpage according to the content that users are interested in, so as to satisfy each user as much as possible. Using sequential pattern analysis to mine logs, we can find the customer's access sequence pattern. In the application of Web usage record mining, sequential pattern mining can be used to capture common navigation paths among user paths. When a user visits an e-commerce website, the website administrator can search out the visitor's access sequence pattern and recommend the pages that the visitor is interested in but has not visited. Sequence pattern analysis can also analyze the order of commodity purchase, so as to make suggestions to customers. For example, when a search engine sends out a query request or browses web information, an advertisement related to the information will pop up. For example, users who buy printers will generally buy printing paper, toner cartridges and other printing consumables soon. An excellent recommendation system will build a exclusive store for customers, and the content of the website will be adjusted according to the characteristics of each customer. We can also analyze the effect of website and product promotion from some excavated sequence patterns.

2) Application of association rules in electronic commerce.

Association rules reveal the implicit relationship between data, and the task of association analysis is to find association rules or related programs between things. The goal of association rule mining is to find out the internal relationship of data information in data items. Mining association rules is to search the relationship among the contents, pages and files accessed by users on the server, thus improving the design of e-commerce websites. It can better organize the website and reduce the burden of users filtering website information. What products are customers likely to buy at the same time when shopping? Association rule technology can analyze customers' shopping habits through the relationship between different goods in the shopping basket. For example, 90% of customers who buy milk also buy bread, which is an association rule. If a store or e-commerce website sells these two products together, its sales will increase. The goal of association rule mining is to analyze the relationship between customer purchases through tools, which is also a typical application of shopping basket data analysis. The association rule is to find the correlation of different items in similar events, such as mobile phone plus charging treasure, mouse plus mouse pad and other buying habits belong to association analysis. Association rule mining technology can find out association rules with corresponding algorithms. For example, in the above example, merchants can improve the placement of goods according to the correlation between goods. When customers buy a mobile phone, they will put the charging treasure into the recommended products. If the probability of buying some goods at the same time is high, it means that these goods are related. Merchants can put these related commodity links together and recommend them to customers, which is beneficial to the sales of commodities. Merchants can also effectively match and buy commodities according to the association, thus improving the management level of commodities. If customers buy lamps, most of them will also buy switch sockets. Therefore, lamps and switch sockets are generally placed in one area for customers to choose. According to the analysis, we can find out the association rules of the goods that customers need, and recommend the needed goods to customers from the results of mining analysis, that is, we can recommend the goods that customers may be interested in, which will greatly improve the sales of goods.

3) Application of path analysis technology in e-commerce.

Path analysis technology is to find out the most frequently visited paths in the website and adjust the website structure by analyzing the times that customers visit the website in the log file of the Web server, so as to help users find the products or information they need as quickly as possible. For example, when a user visits a website, if there are many pages that the user is not interested in, it will affect the user's web browsing speed, thus reducing the user's browsing interest and increasing the maintenance cost of the whole website. Using path analysis technology, we can fully grasp the relationship between the pages of the website and the links between hyperlinks, and get the most frequently visited pages through analysis, thus improving the website structure and page design.

4) Application of classification analysis in e-commerce.

Classification technology plays a very important role in Web analysis applications that model users according to various predefined rules. For example, given a group of users' transactions, the sum of each user's purchase records in a certain period can be calculated. Based on these data, a classification model can be established to classify users into two categories: those who tend to buy and those who don't, as well as the characteristics that need to be considered, such as users' statistical attributes and navigation activities. Classification technology can be used to predict which customers are interested in which promotion methods, and can also predict and classify customers. Through the classification analysis in e-commerce, we can understand the interests and purchase intentions of various customers, thus finding some potential customers, thus providing personalized network services for each type of customers and carrying out targeted business activities. Through the classification and positioning model, it can help decision makers to locate their best customers and potential customers, improve customer satisfaction and loyalty, and maximize customer yield, thus reducing costs and increasing income.

5) The application of cluster analysis in electronic commerce.

Clustering technology can cluster data items with the same characteristics into one class. Cluster analysis is to compare the related data in the database, find out the relationship between the data, and classify the data with different properties and characteristics. The goal of cluster analysis is to collect data and classify them on the basis of similarity. According to the same or similar customer buying behavior and customer characteristics, the market can be effectively segmented by cluster analysis technology, and targeted marketing strategies should be formulated for each type of market after segmentation. There are two kinds of clustering: page clustering and user clustering. User clustering is to establish a group of users with the same browsing mode, which can divide the market in e-commerce or provide personalized web content for users with similar interests. Based on the analysis of users' statistical attributes (such as age, gender, income, etc.), more valuable business intelligence can be found. ) in the user group. In e-commerce, it is to use cluster analysis technology to divide the market in detail. Cluster analysis can divide different customer groups with different customer characteristics according to their purchasing behavior. By clustering customers with similar browsing behaviors, marketers can subdivide customers into categories, which can provide customers with more humanized and caring services. For example, through the analysis of clustering technology, it is found that some customers like to visit the content of web pages about auto parts, so that the content of the website can change dynamically, and the network can automatically send new product information or emails about auto parts to these customers through clustering. Classification and clustering often interact. By clustering customers with similar behaviors or habits in e-commerce, we can provide customers with more satisfactory services. In the analysis, technicians first use cluster analysis to subdivide the data to be analyzed, then use classification analysis to classify and label the data set, and then re-classify the labels, and so on. The two analysis methods are repeated and satisfactory results are obtained.

5 conclusion

With the rapid development of the Internet, the application of big data analysis is more and more extensive. E-commerce plays an increasingly important role in commercial trade. Using web mining technology to mine and process massive commercial data, analyze customers' purchasing preferences, track market changes and adjust sales strategies is of great significance for decision makers to make effective decisions and improve the market competitiveness of enterprises.

References:

[1] Pang Yingzhi. Application of Web data mining technology in e-commerce [J]. Information Science, 20 1 1, 29(2):235-240.

[2] Ma Zongya, Zhang Huiyan. Research on the Application of Web Data Mining Technology in E-commerce [J]. Modern Economic Information, 20 14(6):23-24.

[3] Xu Jianbin. The application of Web data mining technology in e-commerce [J]. Time Finance, 20 13(4):234-235.208.

[4] Zhou Shidong. Research on the Application of Web Data Mining in E-commerce [D]. Beijing Jiaotong University, 2008.

[5] Paragraph. Application of Web data mining technology in e-commerce [J]. Journal of Longdong University, 2009(3):32-34.

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