Application Of Mining Algorithms Research Articles

Application of mining algorithm in personalized Internet marketing strategy in massive data environment

Abstract Internet marketing requires a personalized marketing strategy. In this study, the application of data mining in personalized Internet marketing was studied. Based on the mining algorithm, a personalized marketing method was designed. Through the calculation of frequent closed item sets and support counts of positive and negative samples, the interval with a high success rate for marketing was obtained. With performance analysis, it was found that the success rate of the marketing method proposed in this study improved 8% compared with the traditional marketing method and had a better performance under the smaller interval number and smaller minimum success number. After applying the designed method in telecommunication enterprise A, it was found that after adopting the marketing method of this study, the marketing success rate of enterprise A increased from 2.72 to 6.31%, which indicated the effectiveness of the method. The research results of this study verify the role of data mining algorithms in Internet marketing, which is conducive to the further application of mining algorithms in personalized marketing and innovation of business modes.

An Efficient Algorithm for Data Cleaning of Log File using File Extensions

Wide Web is a monolithic repository of web pages that provides the Internet users with heaps of information. With the growth in number and complexity of Websites, the size of web has become massively large. Web Usage Mining is a division of web mining that involves application of mining techniques to web server logs in order to extract the behavior of users. A Web Usage Mining process comprises of three phases: data preprocessing, patterns discovery and pattern analysis. Data preprocessing tasks are carried out former to the application of mining algorithms. Preprocessing enables to translate the unprocessed data which is composed from server log files into constructive data abstraction. The appropriate analysis of a web server log proves to be beneficiary to manage the websites efficiently from the administrative and users' prospective. Preprocessing results also strongly influences the later phases of Web Usage Mining. This makes the preprocessing of server log files a significant step in Web Usage Mining. This paper emphasizes on the Web Usage Mining process and makes an exploration in the field of data cleaning.