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Clustering analysis is used to explore the classification for large dataset and Canberra distance is generalized so that it can process the data with categorical attributes. Based on the generalized Canberra distance definition, an instance of constraint-based clustering is introduced. Meanwhile, the nearest neighbor classification is improved. Class-labeled clusters are regarded as classifying models...
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...
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...
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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