PG Miner, a graph-based Algorithm for mining frequent closed item sets consists of construction a prefix graph structure and decomposing the database to variable length bit vectors, which are assigned to nodes of the graph. The main advantage of this representation is that the bit vectors at each node are relatively shorter than others existing methods. Use projected databases to prune their non-closed item sets. There are two typical strategies adopted by these algorithms: (1) an effective pruning strategy to reduce the combinational search space of candidate item sets and (2) a compresses data representation to facilitate in-core processing of the item sets
In the area of knowledge discovery in databases, the generalized association rule mining is an exten...
Mining for association rules involves extracting pat-terns from large database and inferring useful ...
Association Rule Mining (ARM) is finding out the frequent itemsets or patterns among the existing it...
ABSTRACT In this paper a new mining algorithm is defined based on frequent item set. Apriori Algor...
The subject of this research is mining data stream. It is one of the most challenging and widely res...
[[abstract]]In this paper, we study the issues of mining and maintaining association rules in a larg...
Discovering association rules that identify relationships among sets of items is an important proble...
[[abstract]]In this paper, we study the issues of mining and maintaining association rules in a larg...
Abstract-- In data mining research, generating frequent items from large databases is one of the imp...
The output of boolean association rule mining algorithms is often too large for manual examination. ...
AbstractÐMining association rules is an important task for knowledge discovery. We can analyze past ...
rule mining; frequent closed itemset Abstract � frequent closed itemset tend to be a condensed repre...
AbstractData mining is used to deal with the huge size of the data stored in the database to extract...
The classical algorithm for mining association rules is low efficiency. Generally there is high redu...
The output of boolean association rule mining algo-rithms is often too large for manual examination....
In the area of knowledge discovery in databases, the generalized association rule mining is an exten...
Mining for association rules involves extracting pat-terns from large database and inferring useful ...
Association Rule Mining (ARM) is finding out the frequent itemsets or patterns among the existing it...
ABSTRACT In this paper a new mining algorithm is defined based on frequent item set. Apriori Algor...
The subject of this research is mining data stream. It is one of the most challenging and widely res...
[[abstract]]In this paper, we study the issues of mining and maintaining association rules in a larg...
Discovering association rules that identify relationships among sets of items is an important proble...
[[abstract]]In this paper, we study the issues of mining and maintaining association rules in a larg...
Abstract-- In data mining research, generating frequent items from large databases is one of the imp...
The output of boolean association rule mining algorithms is often too large for manual examination. ...
AbstractÐMining association rules is an important task for knowledge discovery. We can analyze past ...
rule mining; frequent closed itemset Abstract � frequent closed itemset tend to be a condensed repre...
AbstractData mining is used to deal with the huge size of the data stored in the database to extract...
The classical algorithm for mining association rules is low efficiency. Generally there is high redu...
The output of boolean association rule mining algo-rithms is often too large for manual examination....
In the area of knowledge discovery in databases, the generalized association rule mining is an exten...
Mining for association rules involves extracting pat-terns from large database and inferring useful ...
Association Rule Mining (ARM) is finding out the frequent itemsets or patterns among the existing it...