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Based on data mining Chinese Spam Filtering Research and Implementation

Author: LingZuo
Tutor: FuXiuFen
School: Guangdong University of Technology
Course: Software Engineering
Keywords: Spam Data Mining Bayesian algorithm Mail filter Automatic text word Feature Extraction
CLC: TP393.098
Type: Master's thesis
Year: 2005
Downloads: 281
Quote: 2
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Abstract


With the wide application of e-mail spam as commercial advertising , malicious programs or sensitive content carriers , but also increasingly on the system's security and the lives of people with serious threats , spam has become a global problem with important practical significance issue . Mail automatically filters are rule-based and probabilistic two ways. Naive Bayes algorithm based on probabilistic method is simple , fast speed , classification accuracy , etc., in text classification is widely used. However, in the spam filtering process, the legitimate messages to be mistaken as spam will likely give users a great loss. Traditional Naive Bayes algorithm to categorize messages and filtering, did not fully take into account the legitimate mail and spam that has different characteristics, so for mail filtering has some limitations . In this paper, based on data mining methods for Bayesian spam filtering mail filtering systems research and implementation. In this paper, the main contents include: the current popular anti-spam technology overview , basic principles and their respective comparative advantages and disadvantages of doing research ; take advantage of e-mail filtering based on data mining method - Bayesian algorithm , the establishment of an improved Bayesian spam filtering model ; analysis vector space and feature item selection and extraction principles and methods ; proposes the application based on binary grammatical phrase markers Chinese word segmentation algorithm ; analysis improved feature vector space dimension reduction deal with the problem , reducing the number of feature items ; utilization experiments traditional Naive Bayes algorithm and improved Bayesian spam filtering algorithms are analyzed and compared.

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