Knowledge Discovery Based On Artificial Intelligence

The nature of knowledge discovery is the mathematical calculation in high-dimensional space. The traditional study in human space has lasted through several thousand years, but the study in high-dimensional space has just begun. Nevertheless, through the use of the core technology of artificial intelligence, knowledge discovery has achieved many establishments. The issues that traditional math cannot solve, such as image recognition, and other issues can be resolved to a certain extent. For the future, the development of technology has brought unlimited potential. In this dissert, knowledge and knowledge discovery, artificial intelligence technology and the core algorithm: Back-propagation network and support vector machines to be recalled as detailed as possible. On this basis, this dissert proposed that the the knowledge discovery is facing three major problems: lack of cross-discipline, limited to traditional areas such as science and engineering; lack of ability to handle unstructured data, in particular, WEB or WORD data; no uniform standard of knowledge expression.For the above three issues, the paper design of the three experiments:1 In this dissert , WORD documents, "Shuowen-yu chapter" is used as a data source. By using the rules of extraction, we can get a structured data source. Then use the SVM toolbox of Matlab to start a word classification. Finally, the Z notation is used to describe this definition.2 In this dissert, we get data from www.alltobid.com on the Shanghai license plate records of previous bids by WEB crawl. These data used as a data source, a BP network constructed to fitting functions and forecast the price. At the same time, we get the comparation with the results from traditional methods of economics. Finally, we use Z notation to describe this function.3 As a Master of Management, this dissert reviewed on the parameters evaluation and its various forms, and then combined both BP networks and SVM classification technology, to assess the concept ofthe dynamic parameters evaluation system. The old methods of parameters evaluation were arbitrary, rigid weight and easy to be targeted. In order to avoid the occurrence of this phenomenon, this dissert believe that we should proceed from own samples, samples from their own description of the nature of the problem. First, to use SVM extract the samples' characteristics to drawn parameters; followed, in accordance with the parameters of the sample cycle, the weight to receive the value of each of the last according to different weights; finally weights can be function fitted and forecasted by BP. This constitutes the dynamic parameters evaluation system, whenever a new sample created, the system re-calculation and weight and adjusts parameters. Undoubtedly has a better adaptive capacity and more useful with the practical requirements. This dissert conducted a housing price index for Shanghai, the dynamic parameters of empirical research: We get the the transaction data from real estate trading center in Shanghai (Fangdi.com.cn) by WEB crawl; different regions to impact housing price in Shanghai As a parameter, the extent of how it impact as weight; finally use Z notation to describe the entire dynamic parameters evaluation system..In this dissert, such as the three experimental approach to the issues raised in this dissert was discussed and explained. For cross-disciplinary issues, the paper joint with Chinese subjects, use the SVM technology for the identification; joint with the author's economics background, use BP network to function Fitting the price of license plate auction in Shanghai from WEB data; Finally, as a Master of Management, to assess the concept ofthe dynamic parameters evaluation system by combining the technology of SVM and BP network, to improve the parameters evaluation in management. For unstructured data sources, the dissert use WORD document, WEB data as unstructured data, extracted by the rules, transform the unstructured datato be semi-structured or structured data for knowledge discovery; for knowledge expression, this dissert used the Z notation to descript every experiment.