An Improved Aprior Algorithm Based on Mining Association Rule
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Based on analysis of the classical Apriori algorithm in mining association rule,this paper presents an improved Apriori algorithm to increase the efficiency of generating association rules.The improved algorithm adopts matrix to express database,which can reduce the times of database scanning.The improved algorithm use vectors operation to count frequent item sets and get rid of unwanted data simultaneously.Redundant data are deleted in time to improve the Apriori algorithm.Keywords:
GSP Algorithm
Association (psychology)
Affinity analysis
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Based on the analysis of the classical algorithm for mining association rules,an improved Apriori algorithm of association rules was put forward,whose performance was tested by UCI Machine Learning Data Sets.Besides,a corresponding application of the electronic patient record mining was designed.The results demonstrate that the new Apriori works better in both running speed and mining capability.
GSP Algorithm
Association (psychology)
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Mining association rule is an important task in data mining research field, its purpose is to mine associations in transaction database. Apriori algorithm is a classical algorithm for mining association rule, due to the algorithm need to be repeated scanning the database, and it has less efficiency. Based on the study of principle and efficiency of the Apriori algorithm, this paper proposes an improved strategy based on reducing transaction to optimize the Apriori algorithm,the analysis and comparison is carried out between it and the Apriori algorithm. The experimental result shown that the improved algorithm has a more significant performance than the Apriori algorithm.
GSP Algorithm
Affinity analysis
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Apriori algorithm is a classical algorithm of boolean association rule mining.However,data mining must consider the problem of discovering association rules between items in a large database of sales transactions.In most cases,it produces a great deal of candidates.We present a new algorithm for improving the efficiency of apriori algorithm.
GSP Algorithm
Association (psychology)
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Association rule is one of the key technologies in data mining. In order to overcome the limitations of Apriori algorithm, the most classical algorithm of association rule mining at present, this paper puts forwards a new algorithm in which the database is scanned only once. A practical example is given and, in the end, the implementation of the algorithm is designed.
GSP Algorithm
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Apriori algorithm is the most classical algorithm of all the association rules mining methods,but Apriori algorithm has two(faults,) firstly, this algorithm has to scan database many times,secondly,this algorithm has to produce many candidate set item.This paper improves this algorithm through making code for every item.Making code can reduce the times of scanning database,deleting items can reduce the number of candidate set items,so as to improve the efficiency of Apriori algorithm.The experiment results in the same environment show that the new algorithm can greatly improve the mining efficiency of association rules.
GSP Algorithm
Code (set theory)
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A new idea to improve association rule mining algorithms being based on conditions judgment is proposed,that's all based on thorough analysis of Apriori algorithm and several improved algorithms.This algorithm reduces unnecessary expenses and accelerates data mining by combining transaction compression and candidate item compression.
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Abstract The paper provides a survey of the study in association rule generation. On the basis of mining association rules theory, Apriori algorithm and Apriori-1 algorithm are analyzed, and an Apriori-2 algorithm is proposed. The new Apriori-2 algorithm has the advantage of less record numbers compared with work DB, high efficiency and certain practical significance.
GSP Algorithm
Association (psychology)
Basis (linear algebra)
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Mining the frequent item sets is a key problem in data mining.It is also the core of the algorithm for mining association rules.Therefore,to improve the efficiency of discovering the frequent item sets is the issue in data mining area.After analyzing and studying the improved algorithm based on Apriori on association rule mining,according to the disadvantage of Apriori algorithm,the paper presents an improved strategy to optimize the Apriori algorithm.At last,the future directions of the algorithms for mining frequent item sets are discussed.
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Apriori algorithm is a classical association rule mining algorithm, but it has problems about frequently scanning database and generating a large number of candidate sets. To solve these problems, an improved DC_Apriori algorithm was proposed, which restructured the storage structure of the database, improved connection of frequent item sets, the generation of k-frequent item sets is only need to join the 1-frequent item sets with k-1-frequent item sets, greatly reduced the number of connections and it can directly get frequent item sets by only one pruning operation, effectively avoid the unnecessary invalid candidate sets, and greatly reduce the number of scanning the database and improve the efficiency of frequent item sets generation. It has proved by experiments that the DC_Apriori algorithm is obviously superior to the Apriori algorithm and the MC_Apriori algorithm based on the matrix, whether in small support degree or in the intensive database with large numbers of transactions and items, the running time of DC_Apriori to get the same result is significantly less than the Apriori algorithm and MC_Apriori algorithm based on the matrix.
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This paper analyzes the Apriori algorithm which is the classical algorithm of association rules,discusses the deficiency of this algorithm,and introduces some main improved algorithms and ideas.In addition,it presents a matrix-based improved Apriori algorithm,in order to improve efficiency through reducing the operation on database.
Realization (probability)
Matrix (chemical analysis)
GSP Algorithm
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