A mining algorithm for frequent closed pattern on data stream based on sub-structure compressed in prefix-tree

2016 ◽  
Author(s):  
Fan Muhan ◽  
Shao Sujie ◽  
Rui Lanlan
Keyword(s):  
Data Stream ◽  
Prefix Tree ◽  
Mining Algorithm ◽  
Closed Pattern

Recent Frequent Item Mining Algorithm in a Data Stream Based on Flexible Counter Windows

2013 ◽  
Vol 385-386 ◽  
pp. 1415-1418
Author(s):  
Yan Yang Guo ◽  
Gang Wang ◽  
Feng Mei Hou ◽  
Qing Ling Mei
Keyword(s):  
Data Stream ◽  
Variable Length ◽  
Streaming Data ◽  
Frequent Item ◽  
Mining Algorithm ◽  
Variable Window

In the paper the author introduces FCW_MRFI, which is a streaming data frequent item mining algorithm based on variable window. The FCW_MRFI algorithm can mine frequent item in any window of recent streaming data, whose given length is L. Meanwhile, it divides recent streaming data into several windows of variable length according to m, which is the number of the counter array. This algorithm can achieve smaller query error in recent windows, and can minimize the maximum query error in the whole recent streaming data.

A Novel Efficient Mining Algorithm for Frequent Patterns on Biological Multiple Sequence

2010 ◽  
Vol 44-47 ◽  
pp. 3697-3701
Author(s):  
Wei Liu ◽  
Ling Chen
Keyword(s):  
Short Length ◽  
Experimental Results ◽  
Frequent Patterns ◽  
Multiple Sequence ◽  
Prefix Tree ◽  
Mining Algorithm

In order to overcome the shortcomings of traditional algorithms, the algorithm MSPM was proposed. It used longer patterns for mining, which avoided producing lots of patterns with short length. Meanwhile by the use of prefix tree of primary frequent patterns, we extended the primary patterns which avoided plenty of irrelevant patterns. The experimental results show that MSPM not only improves the performance but also achieves effective mining results.

An Efficient Frequent Patterns Mining Algorithm over Data Streams Based on FPD-Graph

2012 ◽  
Vol 433-440 ◽  
pp. 4457-4462 ◽  
Author(s):  
Jun Shan Tan ◽  
Zhu Fang Kuang ◽  
Guo Gui Yang
Keyword(s):  
Data Streams ◽  
Data Stream ◽  
Pattern Mining ◽  
Frequent Pattern Mining ◽  
Frequent Pattern ◽  
Frequent Patterns ◽  
Data Generation ◽  
Experiment Data ◽  
Mining Algorithm ◽  
Head Node

The design of synopses structure is an important issue of frequent patterns mining over data stream. A data stream synopses structure FPD-Graph which is based on directed graph is proposed in this paper. The FPD-Graph contains list head node FPDG-Head and list node FPDG-Node. The operations of FPD-Graph consist of insert operation and deletion operation. A frequent pattern mining algorithm DGFPM based on sliding window over data stream is proposed in this paper. The IBM synthesizes data generation which output customers shopping a data are adopted as experiment data. The DGFPM algorithm not only has high precision for mining frequent patterns, but also has low processing time.

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