Optimization and realization of parallel frequent item set mining algorithm

2016 ◽  
Author(s):  
Ling Yuan ◽  
Dan Li ◽  
Yuzhong Chen
Keyword(s):  
Frequent Item ◽  
Mining Algorithm

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.

The Research of Improved Apriori Algorithm

2012 ◽  
Vol 263-266 ◽  
pp. 2179-2184 ◽  
Author(s):  
Zhen Yun Liao ◽  
Xiu Fen Fu ◽  
Ya Guang Wang
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Apriori Algorithm ◽  
Rule Mining ◽  
Frequent Item ◽  
Mining Algorithm ◽  
The Times ◽  
Frequent Item Sets ◽  
Candidate Item ◽  
Improved Algorithm

The first step of the association rule mining algorithm Apriori generate a lot of candidate item sets which are not frequent item sets, and all of these item sets cost a lot of system spending. To solve this problem，this paper presents an improved algorithm based on Apriori algorithm to improve the Apriori pruning step. Using this method, the large number of useless candidate item sets can be reduced effectively and it can also reduce the times of judge whether the item sets are frequent item sets. Experimental results show that the improved algorithm has better efficiency than classic Apriori algorithm.

scholarly journals A Hash based Mining Algorithm for Maximal Frequent Item Sets using Hashing

IJARCCE ◽  
2017 ◽  
Vol 6 (3) ◽  
pp. 1040-1044 ◽  
Author(s):  
Vaishali Galav ◽  
Deepak Jain
Keyword(s):  
Frequent Item ◽  
Mining Algorithm ◽  
Frequent Item Sets

Construction and Analysis of Scientific and Technological Personnel Relational Graph for Group Recognition

2021 ◽  
Vol 31 (08) ◽  
pp. 1069-1098
Author(s):  
Dongju Yang ◽  
Xiaojian Wang ◽  
Hanshuo Zhang
Keyword(s):  
Science And Technology ◽  
Frequent Item ◽  
Mining Algorithm ◽  
Source Data ◽  
Relationship Model ◽  
Private Datasets ◽  
Behavior Characteristics ◽  
The Relationship

The key to the in-depth management of science and technology is to model the behavior characteristics of scientific and technological personnel and then find groups by analyzing the diverse associations among them. Aiming at the analysis of team relationship among scientific and technological personnel, this paper proposed a method to recognize the group of scientific and technological personnel based on relational graph. The relationship model of scientific and technological personnel was designed, and based on this, the entity and relationship recognition and extraction are performed on the structured and unstructured source data to construct a relational graph. An improved frequent item mining algorithm based on Hadoop was proposed, which enabled getting the group of scientific and technological personnel by mining and analyzing the data in the relational graph. In this paper, the proposed method was experimented on both open and private datasets, and compared with several classical algorithms. The results showed that the method proposed in this paper has a significant improvement in execution efficiency.

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

2014 ◽  
Vol 9 (1) ◽  
Author(s):  
Yanyang Guo ◽  
Gang Wang ◽  
Fengmei Hou ◽  
Qingling Mei
Keyword(s):  
Data Stream ◽  
Frequent Item ◽  
Mining Algorithm
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