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Data Mining of Large Electronic Commerce Web Site

Author: WangZuoTong
Tutor: ZhouChunGuang;SunCaiTang
School: Jilin University
Course: Software Engineering
Keywords: Large - scale e - commerce website Data Mining Data warehouse Customer decision-making OLAP
CLC: TP311.13
Type: Master's thesis
Year: 2008
Downloads: 578
Quote: 1
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Abstract


Data mining is more popular in recent years, technology has been a lot of development in a variety of industries, but in the process of e-commerce applications, has not been made in the application, this article is based on a fictional e-commerce website a some functions of data mining, has made some progress. Data warehousing and data mining are two different technologies. A data warehouse is different from the database, a new form of data storage, the data in the database to the decision needs to reorganize and store data in the form of a multi-dimensional spatial structure. Data mining is the core technology of knowledge, it is found from the database developed from the artificial intelligence, machine learning. Data warehouse and data mining are two different technologies, but they have in common, both of which are developed in the database on the basis of new technologies, they are decision support. Data warehouse integrated data macro, using historical data to predict; data mining is mining knowledge from the database, but also for decision analysis. Although the data warehouse and data mining to support decision analysis in different ways, but they can be combined to improve the ability of decision analysis. Large amounts of data warehouse data mining technology has as its front-end analysis tools to improve decision-making analysis of the data warehouse. The face of fierce competition, circulation enterprises in order to maintain sustainable profit, there is an urgent need for a powerful information management system to provide comprehensive and timely analysis of data, prepared a comprehensive understanding of business operations management in the shortest time make timely decisions. Data mining model management to understand and analyze customer information in a timely manner, and make decisions in a timely manner, thereby increasing customer retention capacity and ability to obtain. The daily transaction information is massive, large e-commerce sites, from the transaction information, product information effective contact is very important. How to make a lot of potential customers in the process of browsing the web, be a registered user and purchase of goods is also very important process. Previously used traditional reporting systems are often the data on the surface, but the depths of the vast amounts of data potentially contain what rules? What our customers value products interlinkages between how? More in-depth rules The greater the value for decision support, but also more difficult to dig out. Thus, with the development of the times, the traditional reporting system has been unable to meet the growing business needs, enterprises are looking forward to the new technology. Data analysis and data mining era is coming. Practice has proved that, for a large e-commerce sites, in addition to the currently established data warehouse, should be unified, standardized, mass data query Decision Analysis, which can take advantage of data mining based on decision tree to build a large-scale e-commerce website model to solve this problem, to to avoid management in the operation and management decision-making blind judge, active in the face and solve these problems. Modern enterprise management concepts, market, customer relationship management, and knowledge mining, and promote the level of sales and service quality, increase their income, while improving customer satisfaction. A fictional e-commerce website www.e-commerce.com this site database system. Microsoft Decision Trees to create OLAP data mining model, and in accordance with the results of the mining part of its functionality. The entire data warehouse model is divided into the following sections: data acquisition: mainly used for access to sales data, customer data, product data, and often data from the source database and clean, transmission, and add it to the data warehouse database . Data management and multi-dimensional modeling: the establishment of a multidimensional data model, customers, products and sales to refresh the data warehouse in a timely manner to reflect the change of the data source, and the data in the data warehouse for the dump. Each update cycle, reconstruction or full refresh integrated data warehouse is unrealistic, the need for incremental updates that add new data to the data warehouse, existing data must be protected. Analysis and processing: analysis indicators, multi-dimensional data analysis and data mining. Important customer analysis, sales analysis. Information services: The main business recommendations, personalized service and information consulting. In the embodiment of the process of data mining, nearly all require the user to participate To fully realize the automation of the mining process, to present or unrealistic. For example, in the data for this section, require the user to select the database, select the data table, select the property for mining, how to deal with property values. With the excavation of the modeling process is carried out, the data flows from the data source (general database, table or file) to the pattern of results, and the data stream is always moving in a simple and useful change in direction. The entire data mining process to achieve the following functions (1) customer information, customer loyalty. Defined and launched a new discount program, making to improve customer satisfaction and loyalty. (2) customer information, analysis of the overall customer purchase intention and interest in buying, advertising and merchandise to recommend more efficient. (3) have analyzed the customer attributes, overall customer shall be subject to analysis and forecasting. Data mining site, we can draw the following conclusions (1) data mining can quickly identify customer behavior in order to establish the site decision. (2) Data mining can reflect the effect of advertising, timely adjustment of advertising strategy. (3) Data mining can improve the overall design of the site.

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