Distributed Association Rule Mining Research Articles

Association Rules Elicitation for Customer’s Shopping on E-commerce Applications

This research provides an approach for eliciting association rules for customers’ shopping on e-commerce applications based on the Apriori algorithm. The proposed system shows and displays some offers and deals from various branches. The system provides the analytics for the seller as the demand for some varieties and the weakness in other varieties and the whole application is organized on the Cloud. The architecture includes three levels; the front-end, middle, and back-end. The front-end level of the site-based Mobile shopping application is made up of Android Mobile devices, to buy miscellaneous products from various nearby branches. The front-end level also displays the link between items purchased. The middle repository level provides a Web service to generate returns from a relational database. The Exchanged information and data between application and servers is stored in the Cloud. The background level offers a Web server and a MySQL database. In this paper, we propose an architecture that reduces the communication overhead in existing Mobile Agent-based Distributed Association Rule Mining (MAD-ARM).

An Adaptive Privacy Preserving Framework for Distributed Association Rule Mining in Healthcare Databases

It is crucial, while using healthcare data, to assess the advantages of data privacy against the possible drawbacks. Data from several sources must be combined for use in many data mining applications. The medical practitioner may use the results of association rule mining performed on this aggregated data to better personalize patient care and implement preventive measures. Historically, numerous heuristics (e.g., greedy search) and metaheuristics-based techniques (e.g., evolutionary algorithm) have been created for the positive association rule in privacy preserving data mining (PPDM). When it comes to connecting seemingly unrelated diseases and drugs, negative association rules may be more informative than their positive counterparts. It is well-known that during negative association rules mining, a large number of uninteresting rules are formed, making this a difficult problem to tackle. In this research, we offer an adaptive method for negative association rule mining in vertically partitioned healthcare datasets that respects users’ privacy. The applied approach dynamically determines the transactions to be interrupted for information hiding, as opposed to predefining them. This study introduces a novel method for addressing the problem of negative association rules in healthcare data mining, one that is based on the Tabu-genetic optimization paradigm. Tabu search is advantageous since it removes a huge number of unnecessary rules and item sets. Experiments using benchmark healthcare datasets prove that the discussed scheme outperforms state-of-the-art solutions in terms of decreasing side effects and data distortions, as measured by the indicator of hiding failure.

ElGamal Homomorphic Encryption-Based Privacy Preserving Association Rule Mining on Horizontally Partitioned Healthcare Data

In today’s world, life-threatening diseases have become a pre-eminent issue in healthcare due to the higher mortality rate. It is possible to lower this mortality rate by utilizing healthcare intelligence to detect diseases early. Patient’s medical data is stored in the EHR system, which is kept up to date by the healthcare provider. Data mining techniques like Association Rule Mining can detect a patient’s disease from their symptoms using digital healthcare data stored in the EHR system. Association rule mining’s efficacy can be improved by using global data from various EHR systems. It mandates that all EHR systems exchange healthcare records to a central server. When personal health information is made available on an untrusted server, several privacy laws may be violated. As a result, the challenge of privacy preserving distributed healthcare data mining has become a well-known study field in the healthcare industry. This research uses an efficient ElGamal homomorphic encryption technique to protect privacy in a distributed association rule mining. The proposed approach to discover the risk factor of most life-threatening diseases like breast cancer and heart disease with its symptoms and discuss the scope for combating COVID-19. Theoretical analysis of the proposed approach shows that it is efficient and maintains privacy in an insecure communication environment. An experimental study with a real dataset shows the proposed approach’s benefit compared to the local single EHR system results.

Privacy-preserving association rule mining based on electronic medical system

Privacy protection during collaborative distributed association rule mining is an important research, which has been widely used in market prediction, medical research and other fields. In medical research, Domadiya et al. (Sadhana 43(8):127, 2018) focused on mining association rules from horizontally distributed healthcare data to diagnose heart disease. They claimed they proposed a more effective privacy-preserving distributed association rule mining (PPDARM) scheme. However, a serious security scrutiny of the scheme is performed, and we find it vulnerable to protect the support of the itemsets from any electronic health record (EHR) system, which is the most important parameter Domadiya et al. tried to protect. In this paper, we first present the cryptanalysis of the PPDARM scheme proposed by Domadiya et al. as well as some revised performance analyses. Then a new PPDARM scheme with less interactions is proposed to avert the shortcomings of Domadiya et al., using the homomorphic properties of the distributed Paillier cryptosystem to accomplish the cooperative computation. Our scheme allows the directed authority (miner) to obtain the final results rather than all cooperative EHR systems, in case of semi-honest but pseudo EHR systems. Moreover, security analysis and performance evaluation demonstrate our proposal is efficient and feasible.

Privacy Preserving Association Rule Mining on Distributed Healthcare Data: COVID-19 and Breast Cancer Case Study.

Association rule mining can be used in healthcare data mining to provide solutions to life-threatening diseases like recent COVID-19. Due to healthcare data privacy concerns, privacy preserving distributed healthcare data mining becomes the primary focus of medical science research. Recently, Chahar et al. (Sādhanā 42:1997–2007, 2017) proposed privacy preserving distributed association rule mining scheme with insecure communication channels. They used the concept of an elliptic curve-based paillier cryptosystem to achieve privacy, authenticity, and integrity. We observed some security vulnerabilities in their privacy preserving association rule mining scheme when implemented with insecure communication channels. We observed that the security vulnerabilities will result in the disclosure of private data of sites (or participants). Furthermore, we propose a secure version of their scheme to solve the security vulnerabilities with insecure communication channels. Theoretical and experimental analysis shows that the proposed scheme has almost equal computation and communication complexities with better securities. A case study on the effectiveness of the proposed approach in combating COVID-19 coronavirus and Breast Cancer is also discussed.

Distributed Association Rules Mining of Varying Data Partition Size Using Nodesets

Distributed Association Rules Mining of Varying Data Partition Size Using Nodesets

Distributed association rule mining with minimum communication overhead

Distributed association rule mining with minimum communication overhead

PARALLEL AND DISTRIBUTED ASSOCIATION RULE MINING ALGORITHMS: A RECENT SURVEY

Data investigation is an essential key factor now a days due to rapidly growing electronic technology. It generates a large number of transactional data logs from a range of sources devices. Parallel and distributed computing is a useful approach for enhancing the data mining process. The aim of this research is to present a systematic review of parallel association rule mining (PARM) and distributed association rule mining (DARM) approaches. We have observed that the parallelized nature of Apriori, Equivalence class, Hadoop (MapReduce), and Spark proves to be very efficient in PARM and DARM environment. We conclude that this comprehensive review, references cited in this article will convey foremost hypothetical issues and a guideline to the researcher an interesting research direction. The most important hypothetical issue and challenges include the large size of databases, dimensionality of data, indexing schemes of data in the database, data skewness, database location, load balancing strategies, methods of adaptability in incremental databases and orientation of the database.

P-tree oriented association rule mining of multiple data sources

A prominent research area in data mining field is (ARM). As distributed databases emerged, need to mine different patterns across them become necessary and hence distributed ARM algorithms were desired. But these algorithms increased communication complexity and overhead. This paper proposes a new algorithm P-tree oriented distributed association rule mining (PDAM) for mining association rules from distributed databases. This algorithm enables a quicker computation of support counts of item sets. P-tree, a special kind of data structure is used in the algorithm which holds the transactional data. These tree data structures do effective storage of data by employing lossless compression techniques. Message exchange optimisation is proposed in this paper. Both the database scans as well as message exchanges are reduced by the proposed method. It would also reduce the size of average transactions, data sets and message exchanges.

Privacy Preserving Distributed Association Rule Mining Approach on Vertically Partitioned Healthcare Data

The trends of data mining in the healthcare is increased due to the digitization of healthcare with electronic health record (EHR) systems. This generates a huge amount of data on daily basis. Data mining with the healthcare data has given the new direction to medical research for early detection of diseases and improving patient care. Many data mining applications require the integration of data from the different sources. For example, the integration of outpatient medical records and health examination data helps to identify the correlation between abnormal test result and disease. The result of association rule mining on this integrated data helps to build the knowledge center for disease prevention, which facilitate the healthcare provider in follow up treatment and prevention. The integration of data requires the sharing of sensitive information about the patients. Disclosing the sensitive information violates the privacy of patients. In this paper, we tackle the problem of privacy preserving association rule mining in vertically partition healthcare data. Furthermore, we analyze the proposed approach in terms of privacy preservation, communication and computation cost.

P-Tree oriented Association Rule mining of Multiple data sources

A prominent research area in data mining field is (ARM). As distributed databases emerged, need to mine different patterns across them become necessary and hence distributed ARM algorithms were desired. But these algorithms increased communication complexity and overhead. This paper proposes a new algorithm P-tree oriented distributed association rule mining (PDAM) for mining association rules from distributed databases. This algorithm enables a quicker computation of support counts of item sets. P-tree, a special kind of data structure is used in the algorithm which holds the transactional data. These tree data structures do effective storage of data by employing lossless compression techniques. Message exchange optimisation is proposed in this paper. Both the database scans as well as message exchanges are reduced by the proposed method. It would also reduce the size of average transactions, data sets and message exchanges.

Parallel and distributed association rule mining in life science: A novel parallel algorithm to mine genomics data

Association rule mining (ARM) is largely employed in several scientific areas and application domains, and many different algorithms for learning association rules from databases have been introduced. Despite the presence of many existing algorithms, there is still room for the introduction of novel approaches tailored for novel kinds of datasets. Because often the efficiency of such algorithms depends on the type of analyzed dataset. For instance, classical ARM algorithms present some drawbacks for biological datasets produced by microarray technologies in particular containing Single Nucleotide Polymorphisms (SNPs). In particular classical algorithms require large execution times also with small datasets. Therefore the possibility to improve the performance of such algorithms by leveraging parallel computing is a growing research area. The main contributions of this paper are: a comparison among different sequential, parallels and distributed ARM techniques, and the presentation of a novel ARM algorithm, named Balanced Parallel Association Rule Extractor from SNPs (BPARES), that employs parallel computing and a novel balancing strategy to improve response time. BPARES improves performance without loosing in accuracy as well as it handles more efficiently the available computational power and reduces the memory consumption.

Privacy-preserving association rule mining for horizontally partitioned healthcare data: a case study on the heart diseases

In recent years, a trend of electronic health record (EHR) system can be seen increasingly in the hospitals, which has generated huge amount of electronically stored data of patients. Association rule mining technique is very helpful in the numerous applications of healthcare (e.g., correlation between disease and symptoms, disease and offering effective treatment and predicting risks of disease based on the historical data, etc.). The data collected by an EHR system are very important for the medical research. Currently, a patient health report is derived on the basis of a physician’s own experience and on the association rule mining results of a local EHR system maintained by a particular hospital. Association rule mining results will be more accurate if the data of all local EHR systems are integrated and association rule mining is performed. Integration of local EHR systems requires the sharing of local EHR data. Sharing of patient records violates the privacy of patients. Hence, medical research is focused on the problem of mining association rules without sharing of local private EHR data. Privacy-preserving distributed association rule mining (PPDARM) solves this issue by mining the association rules while preserving the privacy of patients. In this paper, an approach for the PPDARM is proposed for collaboratively performing association rule mining by all local EHR systems while preserving the privacy. The proposed approach is also analysed with the heart disease dataset.

Adaptive-Miner: an efficient distributed association rule mining algorithm on Spark

Extraction of valuable data from extensive datasets is a standout amongst the most vital exploration issues. Association rule mining is one of the highly used methods for this purpose. Finding possible associations between items in large transaction based datasets (finding frequent itemsets) is most crucial part of the association rule mining task. Many single-machine based association rule mining algorithms exist but the massive amount of data available these days is above the capacity of a single machine based algorithm. Therefore, to meet the demands of this ever-growing enormous data, there is a need for distributed association rule mining algorithm which can run on multiple machines. For these types of parallel/distributed applications, MapReduce is one of the best fault-tolerant frameworks. Hadoop is one of the most popular open-source software frameworks with MapReduce based approach for distributed storage and processing of large datasets using standalone clusters built from commodity hardware. But heavy disk I/O operation at each iteration of a highly iterative algorithm like Apriori makes Hadoop inefficient. A number of MapReduce based platforms are being developed for parallel computing in recent years. Among them, a platform, namely, Spark have attracted a lot of attention because of its inbuilt support to distributed computations. Therefore, we implemented a distributed association rule mining algorithm on Spark named as Adaptive-Miner which uses adaptive approach for finding frequent patterns with higher accuracy and efficiency. Adaptive-Miner uses an adaptive strategy based on the partial processing of datasets. Adaptive-Miner makes execution plans before every iteration and goes with the best suitable plan to minimize time and space complexity. Adpative-Miner is a dynamic association rule mining algorithm which change its approach based on the nature of dataset. Therefore, it is different and better than state-of-the-art static association rule mining algorithms. We conduct in-depth experiments to gain insight into the effectiveness, efficiency, and scalability of the Adaptive-Miner algorithm on Spark. Available: https://github.com/sanjaysinghrathi/Adaptive-Miner

Intelligent Healthcare by Machine Learning Techniques on Distributed Edge Computing Environment

Telemedicine has become an inevitable trend, and telemedicine systems must be based on the cloud platform in order to make good use of personal medical data to provide various types of personal application services. On the other hand, since intelligent long-term care is an inevitable trend, the cloud platform will become the core of the care system in the future. At the same time, the use of machine learning technology will have unlimited potential in the medical field to improve medical outcomes. In this paper, the cloud-based healthcare with machine learning schemes executing under distributed edge computing environment is examined. The distributed association rules mining, and logistic regression schemes are proposed, and the experiment simulations to verify the feasibility of the distributed implementation are conducted.

Privacy-preserving distributed mining of association rules using Elliptic-curve cryptosystem and Shamir’s secret sharing scheme

Distributed data mining has played a vital role in numerous application domains. However, it is widely observed that data mining may pose a privacy threat to individual’s sensitive information. To address privacy problem in distributed association rule mining (a data mining technique), we propose two protocols, which are securely generating global association rules in horizontally distributed databases. The first protocol uses the notion of Elliptic-curve-based Paillier cryptosystem, which helps in achieving the integrity and authenticity of the messages exchanged among involving sites over the insecure communication channel. It offers privacy of individual site’s information against the involving sites and an external adversary. However, the collusion of two sites may affect the privacy of individuals. To address this problem, we incorporate Shamir’s secret sharing scheme in the second protocol. It provides privacy by preventing colluding sites and external adversary attack. We analyse both protocols in terms of fulfilling the privacy-preserving distributed association rule mining requirements.

Bagging and Distributed Association Rule Mining Techniques for Distributed Data Mining on Homogeneous Datasets

Data Mining (DM) is the process of extracting useful unknown information from data using patterns. Distributed Data Mining (DDM) evolved from DM in recent years to mine geographical distributed data. Mining can be performed either on geographical distributed data with same attribute or different attribute. This paper implements DDM based on bagging and Distributed Association Rule Mining on soy-bean, iris and contact-lens datasets having same attribute across geographical distributed sites. Keywords: Distributed Data Mining; classifier approach; bagging; Distributed Association Rule Mining

MCRS: A course recommendation system for MOOCs

With the popularization development of MOOC platform, the number of online courses grows rapidly. Efficient and appropriate course recommendation can improve learning efficiency. Traditional recommendation system is applied to the closed educational environment in which the quantity of courses and users is relatively stable. Recommendation model and algorithm cannot directly be applied to MOOC platform efficiently. With the light of the characteristics of MOOC platform, MCRS proposed in this paper has made great improvement in the course recommendation model and recommendation algorithm. MCRS is based on distributed computation framework. The basic algorithm of MCRS is distributed association rules mining algorithm, which based on the improvement of Apriori algorithm. In addition, it is useful to mine the hidden courses rules in course enrollment data. Firstly, the data is pre-processed into a standard form by Hadoop. It aims to improve the efficiency of the basic algorithm. Then it mines association rules of the standard data by Spark. Consequently, course recommendation information is transferred into MySQL through Sqoop, which makes timely feedback and improves user’s courses retrieval efficiency. Finally, to validate the efficiency of MCRS, a series of experiments are carried out on Hadoop and Spark, and the results shows that MCRS is more efficient than traditional Apriori algorithm and Apriori algorithm based on Hadoop, and the MCRS is suitable for current MOOC platform.

Distributed Association Rule Mining with Load Balancing in Grid Environment

Distributed Association Rule Mining with Load Balancing in Grid Environment

Performance Evaluation of Distributed Association Rule Mining Algorithms

Association Rule Mining (ARM) is a popular and well researched method for discovering interesting relations between variables in large databases. It is intended to identify strong rules discovered in databases using different measures of interestingness. Most ARM algorithms focus on a sequential or centralized environment where no external communication is required. Distributed ARM algorithms (DARM), aim to generate rules from different data sets spread over various geographical sites; hence, they require external communications throughout the entire process. DARM algorithm efficiency is highly dependent on data distribution. The Classical algorithms used in DARM are Count Distribution Algorithm (CDA), Fast Distributed Mining (FDM) Algorithm and Optimized Distributed Association Mining (ODAM) Algorithm. This paper presents the implementation details and experimental results of above mentioned algorithms. The paper also highlights the issues of message exchange size in a distributed environment of current DARM algorithms that can affect the communication costs in a distributed environment.