scholarly journals Mining Frequent Itemsets with Weights over Data Stream Using Inverted Matrix

2016 ◽  
Vol 8 (10) ◽  
pp. 63-71
Author(s):  
Long Nguyen Hung ◽  
Thuy Nguyen Thi Thu
Keyword(s):  
Data Stream ◽  
Frequent Itemsets ◽  
Mining Frequent Itemsets

Maximal Frequent Itemsets in Data Stream Mining Based on Orderly-Compound Policy

2010 ◽  
Vol 26-28 ◽  
pp. 113-117
Author(s):  
Pei Shuai Chen ◽  
Chong Huan Xu
Keyword(s):  
Data Stream ◽  
Frequent Itemsets ◽  
Space Efficiency ◽  
Time Space ◽  
Algorithm Construct ◽  
Closed Itemsets ◽  
Maximal Frequent Itemsets ◽  
Mining Frequent Itemsets ◽  
Basic Window ◽  
Pruning Technique

Mining maximal frequent itemsets get the advantage of a relatively small number of itemsets. Compared to mining frequent itemsets and mining frequent closed itemsets, such algorithm has higher time and space efficiency. According to the features of data streams and combined sliding window, a new algorithm E-FPMFI which is based on orderly-compound policy for mining maximal frequent itemsets in data stream is proposed. The algorithm based on basic window updates information from data stream flow fragment and scans the stream only once to gain and store it in frequent itemsets list. The algorithm construct FP-tree, then compress orderly FP-tree by merging nodes which has equal minsup in same branch, also uses subset mix pruning technique, avoid superset checking. The experimental results show the algorithm has higher time, space efficiency and good scalability.

scholarly journals Towards a new approach for mining frequent itemsets on data stream

2006 ◽  
Vol 28 (1) ◽  
pp. 23-36 ◽  
Author(s):  
Chedy Raïssi ◽  
Pascal Poncelet ◽  
Maguelonne Teisseire
Keyword(s):  
Data Stream ◽  
Frequent Itemsets ◽  
New Approach ◽  
Mining Frequent Itemsets

New Policy of Maximal Frequent Itemsets in Data Stream Mining

2010 ◽  
Vol 26-28 ◽  
pp. 118-122
Author(s):  
Chong Huan Xu ◽  
Chun Hua Ju
Keyword(s):  
Stream Flow ◽  
Data Stream ◽  
Search Space ◽  
Frequent Itemsets ◽  
Stream Flows ◽  
Algorithm Construct ◽  
Maximal Frequent Itemsets ◽  
Core Idea ◽  
Basic Window ◽  
Pruning Techniques

According to the features of data streams and combined sliding window, a new algorithm A-MFI which is based on self-adjusting and orderly-compound policy for mining maximal frequent itemsets in data stream is proposed. This algorithm which is based on basic window updates information from data stream flow fragments and scans the stream only once to gain and store it in frequent itemsets list when the data stream flows. The core idea of this algorithm: construct self-adjusting and orderly-compound FP-tree, use mixed subset pruning techniques to reduce the search space, merge nodes which has equal minsup in the same branch and compress to generate the orderly-compound FP-tree to avoid superset checking when mining maximal frequent itemsets. The experimental results show that the algorithm has higher efficiency in time and space, and also has good scalability.

Sign in / Sign up

Export Citation Format

Share Document