An Efficient Algorithm for Privacy Preserving Maximal Frequent Itemsets Mining

2011 ◽  
Author(s):  
Yuqing Miao ◽  
Xiaohua Zhang ◽  
Kongling Wu ◽  
Jie Su
Keyword(s):  
Efficient Algorithm ◽  
Privacy Preserving ◽  
Frequent Itemsets ◽  
Frequent Itemsets Mining ◽  
Maximal Frequent Itemsets

scholarly journals Mining Recent Maximal Frequent Itemsets Over Data Streams with Sliding Window

2018 ◽  
Vol 16 (6) ◽  
pp. 961-969 ◽  
Author(s):  
Saihua Cai ◽  
Shangbo Hao ◽  
Ruizhi Sun ◽  
Gang Wu
Keyword(s):  
Data Streams ◽  
Sliding Window ◽  
Frequent Itemsets ◽  
Memory Usage ◽  
Time Cost ◽  
Improved Method ◽  
Huge Number ◽  
Frequent Itemsets Mining ◽  
Series Of Experiments ◽  
Maximal Frequent Itemsets

Abstract: The huge number of data streams makes it impossible to mine recent frequent itemsets. Due to the maximal frequent itemsets can perfectly imply all the frequent itemsets and the number is much smaller, therefore, the time cost and the memory usage for mining maximal frequent itemsets are much more efficient. This paper proposes an improved method called Recent Maximal Frequent Itemsets Mining (RMFIsM) to mine recent maximal frequent itemsets over data streams with sliding window. The RMFIsM method uses two matrixes to store the information of data streams, the first matrix stores the information of each transaction and the second one stores the frequent 1-itemsets. The frequent p-itemsets are mined with “extension” process of frequent 2-itemsets, and the maximal frequent itemsets are obtained by deleting the sub-itemsets of long frequent itemsets. Finally, the performance of the RMFIsM method is conducted by a series of experiments, the results show that the proposed RMFIsM method can mine recent maximal frequent itemsets efficiently

A False Negative Maximal Frequent Itemsets Mining Algorithm over Stream

2011 ◽  
Vol 135-136 ◽  
pp. 21-25
Author(s):  
Hai Feng Li ◽  
Ning Zhang
Keyword(s):  
Real World ◽  
False Negative ◽  
Frequent Itemsets ◽  
Experimental Results ◽  
Mining Algorithm ◽  
Chernoff Bound ◽  
Frequent Itemsets Mining ◽  
Condensed Representations ◽  
Maximal Frequent Itemsets ◽  
Landmark Model

Maximal frequent itemsets are one of several condensed representations of frequent itemsets, which store most of the information contained in frequent itemsets using less space, thus being more suitable for stream mining. This paper focuses on mining maximal frequent itemsets approximately over a stream landmark model. A false negative method is proposed based on Chernoff Bound to save the computing and memory cost. Our experimental results on a real world dataset show that our algorithm is effective and efficient.

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