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Through the in-depth study for the existing Apriori algorithm and its improved algorithms, this paper proposes an optimization program for the Apriori algorithm, and tests it. Experimental results show that this improved Apriori optimization association rule mining algorithm can reduce the time complexity of the original algorithm, especially for large databases, this improved algorithm has more evident...
For large databases, the research on improving the mining performance and precision is necessary, so many focuses of today on association rule mining are about new mining theories, algorithms and improvement to old methods. Association rules mining is a function of data mining research domain and arise many researchers interest to design a high efficient algorithm to mine association rules from transaction...
Association Rule Mining (ARM) is the most essential technique for data mining that mines hidden associations between data in large databases. The most important function of ARM is to find frequent itemsets. Frequent closed itemsets (FCI) is an important condense representation method for frequent itemsets, and because of its importance in recent years, there have been many algorithms implemented for...
Due to the development of information systems and technology, businesses increasingly have the capability to accumulate huge amounts of retail data in large databases. In the recent marketing research, products' discounts have rarely been considered as an important decision variable. Although few researches have analyzed the effect of discount on sales, they ignore its temporal characteristics. That...
To mine the frequent item sets from database conveniently and rapidly, a novel approach for association rules mining is proposed in this paper. In our approach, a vector subspace is build from database and the problem of searching frequent sets in database is transformed into that of searching vectors in vector subspace based binary search. Studies show that our approach is not only simple because...
Privacy and security issues in data mining become an important property in any data mining system. A considerable research has focused on developing new data mining algorithms that incorporate privacy constraints. In this paper, we focus on privately mining association rules in vertically partitioned data where the problem has been reduced to privately computing Boolean scalar products. We propose...
Within the area of association rules mining, previous algorithms, e.g., FP-Growth and Apriori, have been generally accepted with high appraisals respectively. Most of these algorithms decompose the problem of mining association rules into two subproblems: find frequent pattern and generate the desired rules. Therefore, such a decomposition strategy cannot but bring delay problem when the size of database...
Sequential patterns mining is an important research topic in data mining and knowledge discovery. Traditional algorithms for mining sequential patterns are built on the binary attributes databases, which has two limitations. First, it can not concern quantitative attributes; second, only positive sequential patterns are discovered. Mining fuzzy sequential patterns has been proposed to address the...
Data mining is a new method of data analysis, especially very large datasets analysis. Nowadays it is becoming more and more popular in data handling. It is a rapidly growing field, whose development is driven by strong research interests as well as urgent practical, social, and economical needs. However, it generally requires much specialty knowledge. It is not easy for people without specialty knowledge...
Finding prevalent mobile user patterns in large amount of data has been one of the major problems in the area of mobile data mining. Particularly, the algorithms of discovering frequent user's behavior patterns in the mobile agent system have been studied extensively in recent years. The key feature in most of these algorithms is that they use a location log dataset with userpsilas requested services...
The aim of the 5E (5 essentials) framework proposed in this paper is to yield high performance in real-time data mining over the Internet. The essentials are: a) realize the Internet power with object-based parallelism; b) select an inter-object interaction pattern suitable for the problem; c) apply the correct programming model to cut communication overhead; d) equip program objects with mobility...
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