Non-check mining algorithm of maximum frequent patterns in association rules based on FP-tree

2010 ◽  
Vol 30 (7) ◽  
pp. 1922-1925
Author(s):  
Liang HUI ◽  
Xue-zhong QIAN
Keyword(s):  
Association Rules ◽  
Frequent Patterns ◽  
Mining Algorithm

scholarly journals An Associate Rules Mining Algorithm Based on Artificial Immune Network for SAR Image Segmentation

2015 ◽  
Vol 2015 ◽  
pp. 1-14
Author(s):  
Mengling Zhao ◽  
Hongwei Liu
Keyword(s):  
Association Rules ◽  
Large Scale ◽  
Classification Algorithm ◽  
Data Sets ◽  
Immune Network ◽  
Artificial Immune ◽  
Mining Algorithm ◽  
Sar Image Segmentation ◽  
Artificial Immune Network ◽  
Adaptive Pso

As a computational intelligence method, artificial immune network (AIN) algorithm has been widely applied to pattern recognition and data classification. In the existing artificial immune network algorithms, the calculating affinity for classifying is based on calculating a certain distance, which may lead to some unsatisfactory results in dealing with data with nominal attributes. To overcome the shortcoming, the association rules are introduced into AIN algorithm, and we propose a new classification algorithm an associate rules mining algorithm based on artificial immune network (ARM-AIN). The new method uses the association rules to represent immune cells and mine the best association rules rather than searching optimal clustering centers. The proposed algorithm has been extensively compared with artificial immune network classification (AINC) algorithm, artificial immune network classification algorithm based on self-adaptive PSO (SPSO-AINC), and PSO-AINC over several large-scale data sets, target recognition of remote sensing image, and segmentation of three different SAR images. The result of experiment indicates the superiority of ARM-AIN in classification accuracy and running time.

Association Rule Mining in Collaborative Filtering

2017 ◽  
pp. 159-179 ◽  
Author(s):  
Carson K.-S. Leung ◽  
Fan Jiang ◽  
Edson M. Dela Cruz ◽  
Vijay Sekar Elango
Keyword(s):  
Data Mining ◽  
Collaborative Filtering ◽  
Association Rules ◽  
Data Structures ◽  
Association Rule ◽  
Association Rule Mining ◽  
Real Life ◽  
Frequent Patterns ◽  
Rule Mining ◽  
Association Rule Miner

Collaborative filtering uses data mining and analysis to develop a system that helps users make appropriate decisions in real-life applications by removing redundant information and providing valuable to information users. Data mining aims to extract from data the implicit, previously unknown and potentially useful information such as association rules that reveals relationships between frequently co-occurring patterns in antecedent and consequent parts of association rules. This chapter presents an algorithm called CF-Miner for collaborative filtering with association rule miner. The CF-Miner algorithm first constructs bitwise data structures to capture important contents in the data. It then finds frequent patterns from the bitwise structures. Based on the mined frequent patterns, the algorithm forms association rules. Finally, the algorithm ranks the mined association rules to recommend appropriate merchandise products, goods or services to users. Evaluation results show the effectiveness of CF-Miner in using association rule mining in collaborative filtering.

Efficient Implementations for UWEP Incremental Frequent Itemset Mining Algorithm

2021 ◽  
Vol 11 (1) ◽  
pp. 18-37
Author(s):  
Mehmet Bicer ◽  
Daniel Indictor ◽  
Ryan Yang ◽  
Xiaowen Zhang
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Search Space ◽  
Frequent Itemset ◽  
Incremental Algorithm ◽  
Frequent Patterns ◽  
Lazy Evaluation ◽  
Rule Mining ◽  
Implementation Techniques ◽  
Common Technique

Association rule mining is a common technique used in discovering interesting frequent patterns in data acquired in various application domains. The search space combinatorically explodes as the size of the data increases. Furthermore, the introduction of new data can invalidate old frequent patterns and introduce new ones. Hence, while finding the association rules efficiently is an important problem, maintaining and updating them is also crucial. Several algorithms have been introduced to find the association rules efficiently. One of them is Apriori. There are also algorithms written to update or maintain the existing association rules. Update with early pruning (UWEP) is one such algorithm. In this paper, the authors propose that in certain conditions it is preferable to use an incremental algorithm as opposed to the classic Apriori algorithm. They also propose new implementation techniques and improvements to the original UWEP paper in an algorithm we call UWEP2. These include the use of memorization and lazy evaluation to reduce scans of the dataset.

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.

Sign in / Sign up

Export Citation Format

Share Document