Mining Incrementally Closed Itemsets over Data Stream with the Technique of Batch-Update

2019 ◽  
pp. 68-84
Author(s):  
Thanh-Trung Nguyen ◽  
Quang Nguyen ◽  
Ngo Thanh Hung
Keyword(s):  
Data Stream ◽  
Closed Itemsets

scholarly journals An Algorithm for Mining Frequent Closed Itemsets in Data Stream

2012 ◽  
Vol 24 ◽  
pp. 1722-1728
Author(s):  
Caiyan Dai ◽  
Ling Chen
Keyword(s):  
Data Stream ◽  
Closed 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.

An Efficient Algorithm for Mining Frequent Closed Itemsets over Data Stream

2012 ◽  
Vol 151 ◽  
pp. 570-575
Author(s):  
Guo Dong Li ◽  
Ke Wen Xia
Keyword(s):  
Data Mining ◽  
Data Structure ◽  
Data Streams ◽  
Efficient Algorithm ◽  
Data Stream ◽  
New Method ◽  
Closed Itemsets ◽  
Low Efficiency

Aiming at the problem of NewMoment algorithm frequently do leftcheck operation in the data mining process, which leads to the low efficiency of algorithm. In this paper, a new method, called LevelMoment, is proposed to improve the NewMoment algorithm which mines frequent closed itemsets over data streams. In this process, a new data structure that added in level node, called LevelCET, is proposed. On this structure, using level checking strategy and optimum frequent closed items checking strategy can quickly tap all the frequent closed itemsets over data streams. The experiments and analysis show that the algorithm has good performance.

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