Descending Dimension Algorithm for Association Rules Based on SQL Calculation

2014 ◽  
Vol 602-605 ◽  
pp. 3626-3629
Author(s):  
Qing Yun Chi ◽  
Zhen Hao ◽  
Xiu Ying Jiang
Keyword(s):  
Association Rules ◽  
Solution Process ◽  
High Dimensional ◽  
Apriori Algorithm ◽  
Layer By Layer ◽  
Frequent Item ◽  
Database Language ◽  
Traditional Process ◽  
Low Dimensional ◽  
Frequent Item Sets

Apriori algorithm is a classical algorithm for association rules mining. But the algorithm must be connect repeatly and operate steps layer by layer to find frequent item sets. To overcome the shortcomings of Apriori algorithm, relational database language SQL is used proceed from the higher-order item sets and compute the support of item set to find frequent item sets in this paper. The solution process eliminates a large number of duplicate items produced by the connection from low-dimensional to high-dimensional in the traditional process. The calculation is simplified and efficiency improved.

scholarly journals Identification of Risk Factors for Early Childhood Diseases Using Association Rules Algorithm with Feature Reduction

2019 ◽  
Vol 19 (3) ◽  
pp. 154-167 ◽  
Author(s):  
Indah Werdiningsih ◽  
Rimuljo Hendradi ◽  
Barry Nuqoba ◽  
Elly Ana ◽  
Keyword(s):  
Risk Factors ◽  
Early Childhood ◽  
Association Rules ◽  
Principal Component ◽  
Feature Reduction ◽  
Apriori Algorithm ◽  
Childhood Diseases ◽  
Frequent Item ◽  
Computing Performance ◽  
Frequent Item Sets

Abstract This paper introduces a technique that can efficiently identify symptoms and risk factors for early childhood diseases by using feature reduction, which was developed based on Principal Component Analysis (PCA) method. Previous research using Apriori algorithm for association rule mining only managed to get the frequent item sets, so it could only find the frequent association rules. Other studies used ARIMA algorithm and succeeded in obtaining the rare item sets and the rare association rules. The approach proposed in this study was to obtain all the complete sets including the frequent item sets and rare item sets with feature reduction. A series of experiments with several parameter values were extrapolated to analyze and compare the computing performance and rules produced by Apriori algorithm, ARIMA, and the proposed approach. The experimental results show that the proposed approach could yield more complete rules and better computing performance.

scholarly journals A Federated Learning Approach to Frequent Itemset Mining in Cyber-Physical Systems

2021 ◽  
Vol 29 (4) ◽  
Author(s):  
Usman Ahmed ◽  
Gautam Srivastava ◽  
Jerry Chun-Wei Lin
Keyword(s):  
Cyber Physical Systems ◽  
Frequent Itemset ◽  
Security And Privacy ◽  
High Dimensional ◽  
Traditional Methods ◽  
Physical Systems ◽  
Frequent Item ◽  
Low Dimensional ◽  
Frequent Item Sets ◽  
Transactional Data

AbstractEffective vector representation has been proven useful for transaction classification and clustering tasks in Cyber-Physical Systems. Traditional methods use heuristic-based approaches and different pruning strategies to discover the required patterns efficiently. With the extensive and high dimensional availability of transactional data in cyber-physical systems, traditional methods that used frequent itemsets (FIs) as features suffer from dimensionality, sparsity, and privacy issues. In this paper, we first propose a federated learning-based embedding model for the transaction classification task. The model takes transaction data as a set of frequent item-sets. Afterward, the model can learn low dimensional continuous vectors by preserving the frequent item-sets contextual relationship. We perform an in-depth experimental analysis on the number of high dimensional transactional data to verify the developed models with attention-based mechanism and federated learning. From the results, it can be seen that the designed model can help and improve the decision boundary by reducing the global loss function while maintaining both security and privacy.

Cross Sellingusing Association Rule Mining

2014 ◽  
Vol 687-691 ◽  
pp. 1337-1341
Author(s):  
Ran Bo Yao ◽  
An Ping Song ◽  
Xue Hai Ding ◽  
Ming Bo Li
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
New Method ◽  
Experimental Result ◽  
Apriori Algorithm ◽  
Rule Mining ◽  
Frequent Item ◽  
Direct Benefits ◽  
Frequent Item Sets

In the retail enterprises, it is an important problem to choose goods group through their sales record.We should consider not only the direct benefits of product, but also the benefits bring by the cross selling. On the base of the mutual promotion in cross selling, in this paper we propose a new method to generate the optimal selected model. Firstly we use Apriori algorithm to obtain the frequent item sets and analyses the association rules sets between products.And then we analyses the above results to generate the optimal products mixes and recommend relationship in cross selling. The experimental result shows the proposed method has some practical value to the decisions of cross selling.

Improved Apriori Algorithm Based on Compressing Transactional Matrix Multiplication

2011 ◽  
Vol 460-461 ◽  
pp. 409-413
Author(s):  
Yue Shun He ◽  
Ping Du
Keyword(s):  
Association Rules ◽  
Matrix Multiplication ◽  
Apriori Algorithm ◽  
Frequent Item ◽  
Classical Algorithm ◽  
Mining Association Rules ◽  
Low Efficiency ◽  
Frequent Item Sets ◽  
Improved Algorithm

Apriori algorithm is one of the most classical algorithm in association rules, however, the algorithm is low efficiency, such as firstly it needs to repeatedly scan the database, which spends much in I/O. Secondly, it create a large number of 2- candidate itemsets during outputting frequent 2- itemsets. Thirdly, it doesn’t cancel the useless itemsets during outputting frequent k- itemsets. In the paper, it describes an improved algorithm based on the compressed matrices which improve the efficiency during creating frequent k- itemsets on three aspects, which simply scans the database once, after compressed transactional matrix, and by multiplied matrix get the frequent item sets, which effectively improved the efficiency in mining association rules.

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.

AFARTICA

2019 ◽  
Vol 30 (3) ◽  
pp. 71-93
Author(s):  
Saubhik Paladhi ◽  
Sankhadeep Chatterjee ◽  
Takaaki Goto ◽  
Soumya Sen
Keyword(s):  
Threshold Value ◽  
Search Space ◽  
Apriori Algorithm ◽  
The Novel ◽  
Novel Technique ◽  
Frequent Item ◽  
Artificial Cell ◽  
Typical Item ◽  
Support Threshold ◽  
Frequent Item Sets

Frequent item-set mining has been exhaustively studied in the last decade. Several successful approaches have been made to identify the maximal frequent item-sets from a set of typical item-sets. The present work has introduced a novel pruning mechanism which has proved itself to be significant time efficient. The novel technique is based on the Artificial Cell Division (ACD) algorithm which has been found to be highly successful in solving tasks that involve a multi-way search of the search space. The necessity conditions of the ACD process have been modified accordingly to tackle the pruning procedure. The proposed algorithm has been compared with the apriori algorithm implemented in WEKA. Accurate experimental evaluation has been conducted and the experimental results have proved the superiority of AFARTICA over apriori algorithm. The results have also indicated that the proposed algorithm can lead to better performance when the support threshold value is more for the same set of item-sets.

scholarly journals Method for Mid-Long-Term Prediction of Landslides Movements Based on Optimized Apriori Algorithm

2019 ◽  
Vol 9 (18) ◽  
pp. 3819 ◽  
Author(s):  
Wenhao Guo ◽  
Xiaoqing Zuo ◽  
Jianwei Yu ◽  
Baoding Zhou
Keyword(s):  
Prediction Model ◽  
Early Warning ◽  
Computational Efficiency ◽  
Prevention And Control ◽  
Apriori Algorithm ◽  
Mapreduce Framework ◽  
Frequent Item ◽  
And Control ◽  
Frequent Item Sets

In the study of the mid-long-term early warning of landslide, the computational efficiency of the prediction model is critical to the timeliness of landslide prevention and control. Accordingly, enhancing the computational efficiency of the prediction model is of practical implication to the mid-long-term prevention and control of landslides. When the Apriori algorithm is adopted to analyze landslide data based on the MapReduce framework, numerous frequent item-sets will be generated, adversely affecting the computational efficiency. To enhance the computational efficiency of the prediction model, the IAprioriMR algorithm is proposed in this paper to enhance the efficiency of the Apriori algorithm based on the MapReduce framework by simplifying operations of the frequent item-sets. The computational efficiencies of the IAprioriMR algorithm and the original AprioriMR algorithm were compared and analyzed in the case of different data quantities and nodes, and then the efficiency of IAprioriMR algorithm was verified to be enhanced to some extent in processing large-scale data. To verify the feasibility of the proposed algorithm, the algorithm was employed in the mid-long-term early warning study of landslides in the Three Parallel Rivers. Under the same conditions, IAprioriMR algorithm of the same rule exhibited higher confidence than FP-Growth algorithm, which implied that IAprioriMR can achieve more accurate landslide prediction. This method is capable of technically supporting the prevention and control of landslides.

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