A mining algorithm for distributed global maximal frequent itemsets based on Sorted SCan-Tree

2016 ◽  
Author(s):  
Yulei Huang ◽  
Jinhuan Wang ◽  
Yan Li ◽  
Qing Lin
Keyword(s):  
Frequent Itemsets ◽  
Mining Algorithm ◽  
Maximal Frequent Itemsets

Mining algorithm for maximal frequent itemsets based on improved FP-tree

2013 ◽  
Vol 32 (2) ◽  
pp. 326-329 ◽  
Author(s):  
Li-sheng MA ◽  
Guang-shun YAO ◽  
Chuan-jian YANG
Keyword(s):  
Frequent Itemsets ◽  
Mining Algorithm ◽  
Maximal Frequent Itemsets

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.

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.

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