scholarly journals Privacy Preserving Data Mining using Secure Multiparty Computation Based on Apriori and Fp-Tree Structure of Fp-Growth Algorithm

2020 ◽  
Vol 9 (3) ◽  
pp. 353-359
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Secure Multiparty Computation ◽  
Tree Structure ◽  
Multiparty Computation ◽  
Apriori Algorithm ◽  
Rule Mining ◽  
Frequent Item ◽  
Tree Optimization ◽  
Frequent Item Sets

In this work, a method is proposed to deal with secure multiparty computation (SMC) based problems. The computation is done on the grocery dataset collected from three various grocery shops. The privacy is maintained by generating the rules based on FP-Tree algorithm under Association Rule Mining (ARM). Privacy and correctness are the important requirements of SMC. In privacy requirement, the things apart from necessary are not learned. This implies that only output will be learned by the parties. Each party must receive correct output to ensure the correctness. In this work, secure auction is done using SMC and frequent item sets are computed to perform the association rule mining. The most familiar FP-growth schemes have the short fallings like former space complexity and latter time complexity. The performance of the algorithms has been enhanced by using APFT algorithm which is a combined version of FP-tree structure of FP-growth algorithm and Apriori algorithm. The conditional and sub conditional patterns are not generated continuously in APFT. The speed of the APFT is high when compared to Apriori algorithm and FP-growth.The correlated items are included by modifying APFT and noncorrelated item sets are shaped by using APFT. This modification is used for FP-tree optimization. From the frequent item set, the loosely associated items are removed by using this modification. The system implemented is clearly described and its performance is evaluated. The results confirmed that the proposed scheme is extremely effective.

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.

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.

Support-Less Association Rule Mining Using Tuple Count Cube

2007 ◽  
Vol 06 (04) ◽  
pp. 271-280
Author(s):  
Qin Ding ◽  
William Perrizo
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Data Cube ◽  
Apriori Algorithm ◽  
Rule Mining ◽  
High Confidence ◽  
Support Level ◽  
Vertical Data ◽  
Frequent Item Sets ◽  
High Support

Association rule mining is one of the important tasks in data mining and knowledge discovery (KDD). The traditional task of association rule mining is to find all the rules with high support and high confidence. In some applications, we are interested in finding high confidence rules even though the support may be low. This type of problem differs from the traditional association rule mining problem; hence, it is called support-less association rule mining. Existing algorithms for association rule mining, such as the Apriori algorithm, cannot be used efficiently for support-less association rule mining since those algorithms mostly rely on identifying frequent item-sets with high support. In this paper, we propose a new model to perform support-less association rule mining, i.e., to derive high confidence rules regardless of their support level. A vertical data structure, the Peano Count Tree (P-tree), is used in our model to represent all the information we need. Based on the P-tree structure, we build a special data cube, called the Tuple Count Cube (T-cube), to derive high confidence rules. Data cube operations, such as roll-up, on T-cube, provide efficient ways to calculate the count information needed for support-less association rule mining.

Research on Privacy Preserving Data Mining Association Rules Protocol

2013 ◽  
Vol 756-759 ◽  
pp. 1661-1664 ◽  
Author(s):  
Xiao Ming Zhu
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
Privacy Preserving ◽  
Secure Multiparty Computation ◽  
Multiparty Computation ◽  
Rule Mining ◽  
Individual Values ◽  
Privacy Preserving Data Mining ◽  
Future Data

Privacy preserving in data mining is a significant direction. There has been growing interests in private concerns for future data mining research. Privacy preserving data mining concentrates on developing accurate models without sharing precise individual data records. A privacy preserving association rule mining algorithm was introduced. This algorithm preserved privacy of individual values by computing scalar product. Then, the data mining and secure multiparty computation are briefly introduced. And proposes an implementation for privacy preserving mining protocol based secure multiparty computation protocol.

Ordering Policy Using Multi-Level Association Rule Mining

2018 ◽  
Vol 11 (4) ◽  
pp. 84-101 ◽  
Author(s):  
Reshu Agarwal ◽  
Sarla Pareek ◽  
Biswajit Sarkar ◽  
Mandeep Mittal
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Order Quantity ◽  
Rule Mining ◽  
Ordering Policy ◽  
Optimum Order ◽  
Frequent Items ◽  
Multi Level ◽  
Real Scenario ◽  
Frequent Item Sets

In this article, an inventory model for a retailer's ordering policy is studied. Multi-level association rule mining is used to find frequent item-sets at each level by applying different threshold at different levels. During order quantity estimation, category, content, and brand of the items are considered, which leads to the discovery of more specific and concrete knowledge of the required order quantity. At each level, optimum order quantity of frequent items is determined. This assists inventory manager to order optimal quantity of items as per the actual requirement of the item with respect to their category, content and brand. An example is devised to explain the new approach. Further, to understand the effect of above approach in the real scenario, experiments are conducted on the exiting dataset.

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