scholarly journals Distributed FP Growth Algorithm for Cloud Platform without Exposing the Individual Transaction Data

2019 ◽  
Vol 8 (3) ◽  
pp. 4983-4989
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Distributed Database ◽  
Privacy Preserving ◽  
Current Paper ◽  
Cloud Platform ◽  
Transaction Data ◽  
Powerful Technique ◽  
Hidden Patterns ◽  
The Individual

Data mining is a concept of extracting the required patterns to take appropriate decisions. One of the major challenges in data mining is to extract hidden patterns with the secure and privacy from the huge databases. Privacy preserving is a method used to extract hidden patterns with privacy. In this paper Mining Association rules with privacy preserving mechanism in the cloud platform is proposed. It is a powerful technique to find the hidden pattern in the distributed database. For now many mechanisms has proposed but it has many drawback, not proven and not specific. In cloud the data is stored in the servers. The data is distributed in different servers in cloud platform. Each server has one of the transaction data. The current paper proposed the distributed FP growth algorithm for cloud platform without exposing the individual transaction data. The results proved that the proposed algorithm is best to extract hidden pattern from Cloud platform in terms of efficiency.

Privacy-Preserving Mining of Association Rules for Smart Grid

2014 ◽  
Vol 971-973 ◽  
pp. 1692-1695
Author(s):  
Huan Ren ◽  
Lu Li ◽  
Liu Sheng Huang ◽  
Wei Yang
Keyword(s):  
Data Mining ◽  
Smart Grid ◽  
Association Rules ◽  
Distribution Network ◽  
Privacy Preserving ◽  
Electricity Distribution ◽  
Individual Data ◽  
Smart Meters ◽  
Privacy Concerns ◽  
The Individual

The widespread deployment of smart meters for the modernization of the electricity distribution network has been associated with privacy concerns due to the potentially large number of measurements that reflect the consumers’ behavior. At the same time, how to extract important knowledge from the potentially large of measurements — these measurements are spilt among various parties, has already became a hot topic in the field of data mining. In this paper, we present protocols that can be used to compute meter measurements over defined sets of meters without revealing any additional about the individual meter readings, and address secure mining of association rules. Thus, most of the benefits of the smart grid can be achieved without revealing individual data.

scholarly journals IMPLEMENTASI ALGORITMA APRIORI TERHADAP DATA PENJUALAN PADA PERUSAHAAN RETAIL

2019 ◽  
Vol 15 (1) ◽  
pp. 85-90 ◽  
Author(s):  
Jordy Lasmana Putra ◽  
Mugi Raharjo ◽  
Tommi Alfian Armawan Sandi ◽  
Ridwan Ridwan ◽  
Rizal Prasetyo
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
A Priori ◽  
Apriori Algorithm ◽  
Business World ◽  
Transaction Data ◽  
Retail Company ◽  
Special Strategy

The development of the business world is increasingly rapid, so it needs a special strategy to increase the turnover of the company, in this case the retail company. In increasing the company's turnover can be done using the Data Mining process, one of which is using apriori algorithm. With a priori algorithm can be found association rules which can later be used as patterns of purchasing goods by consumers, this study uses a repository of 209 records consisting of 23 transactions and 164 attributes. From the results of this study, the goods with the name CREAM CUPID HEART COAT HANGER are the products most often purchased by consumers. By knowing the pattern of purchasing goods by consumers, the company management can increase the company's turnover by referring to the results of processing sales transaction data using a priori algorithm

Comprehensive Survey on Privacy Preserving Association Rule Mining: Models, Approaches, Techniques and Algorithms

2014 ◽  
Vol 23 (05) ◽  
pp. 1450004 ◽  
Author(s):  
Ibrahim S. Alwatban ◽  
Ahmed Z. Emam
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Research Area ◽  
Privacy Preserving ◽  
Rule Mining ◽  
Privacy Preserving Data Mining ◽  
Comprehensive Survey ◽  
New Research

In recent years, a new research area known as privacy preserving data mining (PPDM) has emerged and captured the attention of many researchers interested in preventing the privacy violations that may occur during data mining. In this paper, we provide a review of studies on PPDM in the context of association rules (PPARM). This paper systematically defines the scope of this survey and determines the PPARM models. The problems of each model are formally described, and we discuss the relevant approaches, techniques and algorithms that have been proposed in the literature. A profile of each model and the accompanying algorithms are provided with a comparison of the PPARM models.

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 Hiding Sensitive Association Rules over Privacy Preserving Distributed Data Mining

2014 ◽  
Vol 9 (1) ◽  
pp. 59-72
Author(s):  
Alaa Khalil Jumaa ◽  
Sufyan T. F. Al-Janabi ◽  
Nazar Abedlqader Ali
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Privacy Preserving ◽  
Distributed Data Mining ◽  
Distributed Data

scholarly journals Analisis Pola Pembelian Obat di Apotek Sekar Adi Menggunakan Metode Algoritma Apriori Depok

2020 ◽  
Vol 4 (1) ◽  
pp. 112
Author(s):  
Siti Awaliyah Rachmah Sutomo ◽  
Frisma Handayanna
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Association Rules ◽  
High Frequency ◽  
Apriori Algorithm ◽  
Transaction Data ◽  
Mining Methods ◽  
Using Data ◽  
Purchase Patterns

By using data mining methods can be processed to obtain information and assist in decision making, the amount of data on sales transactions in each drug purchase can cause a data accumulation and various problems, such as drug stock inventory, and sales transaction data, with Data mining techniques, the behavior of consumers in making transactions of drug purchase patterns can be analyzed, It can be known what drugs are commonly purchased by mostly people, the application of Apriori Algorithm is expected to help in forming a combination of itemset. The process of determining drug purchase patterns can be carried out by applying the Appriori algorithm method, determination of drug purchase patterns can be done by looking at the results of the consumer's tendency to buy drugs based on a combination of 3 itemset. By calculating the Analysis of High Frequency Patterns and the Formation of Association Rules, with a minimum of 30% support, there is a combination of 3 itemsset namely MOLAGIT PER TAB (M1), VIT C TABLET (V2), and PARACETAMOL 500 MG TABLET (P2) with 33.33 % support results obtained, and with minimum confidence of 65% there are 6 final association rules.

Privacy Preserving in Association Rules Mining with VPA Algorithm

2012 ◽  
Vol 263-266 ◽  
pp. 3060-3063 ◽  
Author(s):  
Yi Tao Zhang ◽  
Wen Liang Tang ◽  
Cheng Wang Xie ◽  
Ji Qiang Xiong
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Privacy Preserving ◽  
Frequent Itemset ◽  
Apriori Algorithm ◽  
Privacy Preserving Data Mining ◽  
Association Rules Mining ◽  
Synthetic Datasets

A VPA algorithm is proposed to mining the association rules in the privacy preserving data mining, where data is vertically partitioned. The VSS protocol was used to encrypt the vertically data, which was owned by different parties. And the private comparing protocol was adopted to generate the frequent itemset. In VPA the ID numbers of the recordings were employed to keep the consistency of the data among different parties, which were saved in ID index array. The VPA algorithm can generate association rules without violating the privacy. The performance of the scheme is validated against representative real and synthetic datasets. The results reveal that the VPA algorithm can do the same in finding frequent itemset and generating the consistent rules, as it did in Apriori algorithm, in which the data were vertically partitioned and totally encrypted.

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