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Chapter and Conference Paper
Tight Correlated Item Sets and Their Efficient Discovery
We study the problem of mining correlated patterns. Correlated patterns have advantages over associations that they cover not only frequent items, but also rare items.Tight correlated item sets is a concise re...
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Chapter and Conference Paper
Mining Maximal Correlated Member Clusters in High Dimensional Database
Mining high dimensional data is an urgent problem of great practical importance. Although some data mining models such as frequent patterns and clusters have been proven to be very successful for analyzing ver...
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Chapter and Conference Paper
Adapting OLAP Analysis to the User’s Interest Through Virtual Cubes
The manually performing of the operators turns OLAP analysis a tedious procedure. The huge user’s exploration space is the major reason of this problem. Most methods in the literature are proposed in the data ...
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Chapter and Conference Paper
Dynamic Construction of User Defined Virtual Cubes
OLAP provides an efficient way for business data analysis. However, most up-to-date OLAP tools often make the analysts lost in the sea of data while the analysts usually focus their interest on a subset of the...