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A method for encoding database is put forward in this paper. By this way, a record is denoted by only one binary number and so the size of the database is reduced sharply. If the database-encoding algorithm is used into some known modified algorithms, the efficiency will be improved remarkably. At the meantime, a new algorithm, anti-Apriori, which based on the proposed encoding method is introduced...
In this paper, we propose a new efficient data reduction algorithm through combining lattice with rough set. On the basis of lattice learning, the algorithm applies the concept of attribute reduction in the theory of rough sets and calculates the importance degree of attributes automatically by a density based approach. Under acceptable classification precision and complexity, it reduces row and column...
This paper presents a mechanism called R_Apriori for learning rules from large datasets. The existing rough set based methods are not applicable for large data sets for its high time and space complexity. In this paper, large data sets are divided into several parts, in combination with Apriori algorithm, implicated rules are derived in liner relation to size of data set. At last, experiment result...
Outlier detection is one of the branches of data mining, with important applications in the domains of finance fraud detection, network intrusion analysis and so on. But most applications are high dimensional domains. Many algorithms use the concept of proximity to find outliers based on the relationship to the data set. However, the sparsity of high dimensional points results to the algorithms are...
A new kind of clustering algorithm called LOCAHID is presented in this paper. LOCAHID views each potential cluster as a tight coupling structure, which can be described by a density tree. Every density tree is dynamically generated according to its local density distribution. Those "closer" clusters are merged if some conditions are satisfied. In order to extend its applications to large...
DBSCAN is a typical clustering algorithm, which can discover clusters with any arbitrary shape and handle noise well. However, it is also slow in comparison due to neighborhood query for each object and faces difficulty in setting density threshold properly. In this paper, a fast density-based clustering algorithm is presented based on DBSCAN. After sorting objects by a certain dimensional coordinates,...
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