Kavosh: an effective Map-Reduce-based association rule mining method

Barkhordari, Mohammadhossein; Niamanesh, Mahdi

doi:10.1186/s40537-018-0129-4

Cited by 14 publications

(3 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this paper the performance metrics used is execution time and it is evaluated based on support, dimensions and number of nodes. The proposed method is compared with the existing parallel FP-Growth algorithm Kavosh (11) . In the proposed work to perform Association rule mining using FP-Growth algorithm, three different tasks are submitted as three different jobs to Hadoop MapReduce.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

An efficient FP-Growth based association rule mining algorithm using Hadoop MapReduce

Senthilkumar¹,

Prasad²

2020

IJST

View full text Add to dashboard Cite

Objectives: To achieve improved performance of FP-Growth based Association Rule Mining algorithm for massive data by effective utilization of storage, execution capability and improved partition technique within the Hadoop MapReduce framework. Methodology: The proposed methodology has four main phases: In the first phase, the item sets for finding the frequent pattern are encoded and thus minimizes the expensive operation for large data set. In the second phase, improved hash partitioning reduces the network overhead and improves the communication speed within the MapReduce phase for each item set. The effective usage of network bandwidth and storage is obtained by the impact of compression in the third phase. The use of combiner in final phase for frequent item set mining minimizes the overhead of reduce phase by finding the pattern in each partition and minimizes the overall execution time of the FP-Growth algorithm. Findings: FP-Growth based association rule mining algorithm is designed for parallel execution on distributed cluster of servers. Changes to the MapReduce implementation of FP-Growth with the impact of encoding. Improved hash partitioning, compression and configuration results in a significant performance gain with better improvement in execution time. Novelty/Improvements: According to the experimental results, the changes in storage and processing level within the MapReduce framework improves the overall performance of the parallel frequent item set mining in Hadoop cluster.

show abstract

Section: Discussionmentioning

confidence: 99%

“…In (11) , authors proposed Kavosh: An effective Map-Reduce based association rule mining method. When dealing with large volume of data in a map reduce environment the issues faced are data locality and skewness.…”

Section: Background Studymentioning

confidence: 99%

An efficient FP-Growth based association rule mining algorithm using Hadoop MapReduce

Senthilkumar¹,

Prasad²

2020

IJST

View full text Add to dashboard Cite

show abstract

“…Making nodes independent by data format unification can be properly used in Map-Reduce method to solve general problems like graph (Barkhordari et al, 2017) and data mining (Barkhordari et al, 2014) or specific problems like patient similarity (Barkhordari et al, 2015). For future works, this idea can be used for stream processing, real time query processing and online data mining.…”

Section: Discussionmentioning

confidence: 99%

Aras

Barkhordari¹,

Niamanesh²

2017

International Journal of Distributed Systems and Technologies

View full text Add to dashboard Cite

Because of to the high rate of data growth and the need for data analysis, data warehouse management for big data is an important issue. Single node solutions cannot manage the large amount of information. Information must be distributed over multiple hardware nodes. Nevertheless, data distribution over nodes causes each node to need data from other nodes to execute a query. Data exchange among nodes creates problems, such as the joins between data segments that exist on different nodes, network congestion, and hardware node wait for data reception. In this paper, the Aras method is proposed. This method is a MapReduce-based method that introduces a data set on each mapper. By applying this method, each mapper node can execute its query independently and without need to exchange data with other nodes. Node independence solves the aforementioned data distribution problems. The proposed method has been compared with prominent data warehouses for big data, and the Aras query execution time was much lower than other methods.

show abstract

A fuzzy rough set-based horse herd optimization algorithm for map reduce framework for customer behavior data

Sudha,

Krishnamurthy

2024

Knowl Inf Syst

View full text Add to dashboard Cite

Kavosh: an effective Map-Reduce-based association rule mining method

Cited by 14 publications

References 22 publications

An efficient FP-Growth based association rule mining algorithm using Hadoop MapReduce

An efficient FP-Growth based association rule mining algorithm using Hadoop MapReduce

Aras

A fuzzy rough set-based horse herd optimization algorithm for map reduce framework for customer behavior data

Contact Info

Product

Resources

About