Distributed and Shared Memory Algorithm for Parallel Mining of Association Rules

2007 ◽  
pp. 349-363 ◽  
Author(s):  
J. Hernández Palancar ◽  
O. Fraxedas Tormo ◽  
J. Festón Cárdenas ◽  
R. Hernández León
Keyword(s):  
Shared Memory ◽  
Association Rules ◽  
Parallel Mining

Parallel mining and application of fuzzy association rules

2006 ◽  
Vol 1 (2) ◽  
pp. 177-182
Author(s):  
Jian-jiang Lu ◽  
Bao-wen Xu ◽  
Xiao-feng Zou ◽  
Da-zhou Kang ◽  
Yan-hui Li ◽  
...  
Keyword(s):  
Association Rules ◽  
Fuzzy Association Rules ◽  
Parallel Mining

ASSOCIATIVE CLASSIFICATION OF MAMMOGRAMS BASED ON PARALLEL MINING OF IMAGE BLOCKS

2012 ◽  
Vol 24 (06) ◽  
pp. 513-524
Author(s):  
Mohsen Alavash Shooshtari ◽  
Keivan Maghooli ◽  
Kambiz Badie
Keyword(s):  
Association Rules ◽  
Classification Systems ◽  
Classification Model ◽  
Automated Classification ◽  
Associative Classification ◽  
Classification Problems ◽  
Association Rules Mining ◽  
Parallel Mining ◽  
Transactional Databases ◽  
Unique Decision

One of the main objectives of data mining as a promising multidisciplinary field in computer science is to provide a classification model to be used for decision support purposes. In the medical imaging domain, mammograms classification is a difficult diagnostic task which calls for development of automated classification systems. Associative classification, as a special case of association rules mining, has been adopted in classification problems for years. In this paper, an associative classification framework based on parallel mining of image blocks is proposed to be used for mammograms discrimination. Indeed, association rules mining is applied to a commonly used mammography image database to classify digital mammograms into three categories, namely normal, benign and malign. In order to do so, first images are preprocessed and then features are extracted from non-overlapping image blocks and discretized for rule discovery. Association rules are then discovered through parallel mining of transactional databases which correspond to the image blocks, and finally are used within a unique decision-making scheme to predict the class of unknown samples. Finally, experiments are conducted to assess the effectiveness of the proposed framework. Results show that the proposed framework proved successful in terms of accuracy, precision, and recall, and suggest that the framework could be used as the core of any future associative classifier to support mammograms discrimination.

Research of Association Rules Algorithm Based on Matrix under Cloud Computing

2014 ◽  
Vol 568-570 ◽  
pp. 798-801
Author(s):  
Ye Qing Xiong ◽  
Shu Dong Zhang
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Big Data ◽  
Association Rules ◽  
Apriori Algorithm ◽  
Computing Technology ◽  
Binary Matrix ◽  
Transaction Data ◽  
Big Data Mining ◽  
Parallel Mining

It occurs time and space performance bottlenecks when traditional association rules algorithms are used to big data mining. This paper proposes a parallel algorithm based on matrix under cloud computing to improve Apriori algorithm. The algorithm uses binary matrix to store transaction data, uses matrix "and" operation to replace the connection between itemsets and combines cloud computing technology to implement the parallel mining for frequent itemsets. Under different conditions, the simulation shows it improves the efficiency, solves the performance bottleneck problem and can be widely used in big data mining with strong scalability and stability.

scholarly journals Dynamic Parallel Mining Algorithm of Association Rules Based on Interval Concept Lattice

Mathematics ◽  
2019 ◽  
Vol 7 (7) ◽  
pp. 647
Author(s):  
Yafeng Yang ◽  
Ru Zhang ◽  
Baoxiang Liu
Keyword(s):  
Association Rules ◽  
Time Complexity ◽  
Concept Lattice ◽  
Massive Data ◽  
Minimal Cost ◽  
Classical Concept ◽  
Mining Algorithm ◽  
Parallel Mining ◽  
Expansion Form

An interval concept lattice is an expansion form of a classical concept lattice and a rough concept lattice. It is a conceptual hierarchy consisting of a set of objects with a certain number or proportion of intent attributes. Interval concept lattices refine the proportion of intent containing extent to get a certain degree of object set, and then mine association rules, so as to achieve minimal cost and maximal return. Faced with massive data, the structure of an interval concept lattice is more complex. Even if the lattice structures have been united first, the time complexity of mining interval association rules is higher. In this paper, the principle of mining association rules with parameters is studied, and the principle of a vertical union algorithm of interval association rules is proposed. On this basis, a dynamic mining algorithm of interval association rules is designed to achieve rule aggregation and maintain the diversity of interval association rules. Finally, the rationality and efficiency of the algorithm are verified by a case study.

Sign in / Sign up

Export Citation Format

Share Document