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The Research on the Algorithms of Mining Association Rules

Author: ZhuQing
Tutor: QiaHan·HeZiEr
School: Xinjiang Agricultural University
Course: Agricultural Mechanization Engineering
Keywords: Data mining Association rule Apriori Frequent itemset Directed association graph
CLC: TP311.13
Type: Master's thesis
Year: 2010
Downloads: 137
Quote: 0
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Abstract


Data mining is dedicated to data analysis and understanding of the data which can find hidden within the date knowledge. Data mining association rule mining as an important area of research branch, whose main goal is to find a set of database objects associated with some interesting or related links. In recent years, association rule mining data mining technology has become a hot research topic in its results are widely used in marketing, business decisions, administrative management. In the study of mining association rules mining algorithm is the focus. Therefore, we must study and improve existing algorithms, it has a broader application of space. The paper focuses on association rule mining algorithms are studied.Paper introduces data mining technologies, including the basic theory of data mining, data mining functions and application of data mining and data mining technology currently facing some problems. Then, in this based on the Apriori association rule mining algorithm to do a thorough study and analysis, and explained with concrete examples of the implementation process of Apriori algorithm, Apriori algorithm to identify the existing shortcomings, other scholars are also presented on Apriori Algorithm technology. The traditional algorithm for mining frequent item sets that exist in a large number of candidate sets generated, multiple search the database to find candidate itemsets support and other issues, this paper has the associated graph based on frequent itemsets mining algorithm’s basic idea is Based on the original binary encoding of information stored in the database associated with the diagram, through the directed graph associated with the database search identified all the frequent itemsets. Algorithm scans the database only in the implementation time, reduce the I/O operations, combined with examples of the implementation process of the algorithm described in detail, and the improved algorithm is given the time and space complexity. Finally, using Visual C++programming language based on the algorithm, using three data sets tested, the test results are analyzed.

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