Add to ORKGAbstract: Mining maximum frequent itemsets is a key problem in data mining field with numerous important applications. The existing algorithms of mining maximum frequent itemsets are based on local databases, and very little work has been done in distributed databases. However, using the existing algorithms for the maximum frequent itemsets or using the algorithms proposed for the global frequent itemsets needs to generate a lots of candidate itemsets and requires a large amount of communication overhead. Therefore, this paper proposes an algorithm for fast mining global maximum frequent itemsets (FMGMFI), which can conveniently get the global frequency of any itemset from the corresponding paths of every local FP-tree by using frequent pat...
Mining maximal frequent patterns (MFPs) is an approach that limits the number of frequent patterns (...
Mining frequent patterns in large transactional databases is a highly researched area in the field o...
There have been many studies on efficient discovery of frequent patterns in large databases. The usu...
Abstract: During the process of mining maximal frequent item sets, when minimum support is little, s...
The use of frequent itemsets has been limited by the high computational cost as well as the large nu...
While frequent pattern mining is fundamental for many data mining tasks, mining maximal frequent ite...
In this paper, we propose a new method for mining maximal frequent itemsets. Our method introduces a...
Efficient algorithms for mining frequent itemsets are crucial for mining association rules and for o...
Abstract. A fundamental task of data mining is to mine frequent itemsets. Since the number of freque...
As many large organizations have multiple data sources and the scale of dataset becomes larger and l...
With the large amount of data collected in various applications, data mining has become an essential...
This paper introduces an approach for incremental maximal frequent pattern (MFP) mining in sparse bi...
Data mining defines hidden pattern in data sets and association between the patterns. In data mining...
Data Mining is one of the central activities associated with understanding and exploiting the world...
There have been many studies on efficient discovery of maximal frequent itemsets in large databases....
Mining maximal frequent patterns (MFPs) is an approach that limits the number of frequent patterns (...
Mining frequent patterns in large transactional databases is a highly researched area in the field o...
There have been many studies on efficient discovery of frequent patterns in large databases. The usu...
Abstract: During the process of mining maximal frequent item sets, when minimum support is little, s...
The use of frequent itemsets has been limited by the high computational cost as well as the large nu...
While frequent pattern mining is fundamental for many data mining tasks, mining maximal frequent ite...
In this paper, we propose a new method for mining maximal frequent itemsets. Our method introduces a...
Efficient algorithms for mining frequent itemsets are crucial for mining association rules and for o...
Abstract. A fundamental task of data mining is to mine frequent itemsets. Since the number of freque...
As many large organizations have multiple data sources and the scale of dataset becomes larger and l...
With the large amount of data collected in various applications, data mining has become an essential...
This paper introduces an approach for incremental maximal frequent pattern (MFP) mining in sparse bi...
Data mining defines hidden pattern in data sets and association between the patterns. In data mining...
Data Mining is one of the central activities associated with understanding and exploiting the world...
There have been many studies on efficient discovery of maximal frequent itemsets in large databases....
Mining maximal frequent patterns (MFPs) is an approach that limits the number of frequent patterns (...
Mining frequent patterns in large transactional databases is a highly researched area in the field o...
There have been many studies on efficient discovery of frequent patterns in large databases. The usu...