An Extension of the Apriori Algorithm for Finding Frequent Items

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

Karimtabar Noorollah; Shayegan Fard Mohammad Javad

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Seminar Paper

Paper Information

Seminar: INTERNATIONAL CONFERENCE ON WEB RESEARCH
Year:2020 | Issue:6

Download Full-Text

View:

101

Download:

Information Seminar Paper

Title

An Extension of the Apriori Algorithm for Finding Frequent Items

Author(s)

Karimtabar Noorollah | Shayegan Fard Mohammad Javad

Keywords

Data Mining

Apriori Algorithm

Intelligent Method

Frequent Itemset

Abstract

The main purpose of Data Mining is to discover hidden and valuable knowledge from data. The Apriori Algorithm is inefficient due to bulky deals of searching in a dataset. Bearing this in mind, this paper proposes an improved algorithm from Apriori using an Intelligent Method. Proposing an Intelligent Method in this study is to fulfill two purposes: First, we demonstrated that to create itemsets, instead of adding one item at each step, several items could be added. With this operation, the number of k-itemset steps will decline. Secondly, we have proved that by storing the transaction number of each itemset, there would be a diminishment in the time required for the dataset searches to find the frequent k-itemset in each step. To evaluate the performance, the Intelligent Apriori (IAP) algorithm has been compared with the MDC algorithm. The results of this experiment exhibit that since the transaction scans used to obtain the itemset momentously reduced in number, there was a considerable fall in the runtime needed to obtain a Frequent Itemset by the proposed algorithm. In this study, the time required to generate frequent items had a 46% reduction compared to that of the MDC_Apriori Algorithm.

Video

Cites

No record.

References

No record.

Cite

APA: Copy

Karimtabar, Noorollah, & Shayegan Fard, Mohammad Javad. (2020). An Extension of the Apriori Algorithm for Finding Frequent Items. INTERNATIONAL CONFERENCE ON WEB RESEARCH. SID. https://sid.ir/paper/949227/en

Vancouver: Copy

Karimtabar Noorollah, Shayegan Fard Mohammad Javad. An Extension of the Apriori Algorithm for Finding Frequent Items. 2020. Available from: https://sid.ir/paper/949227/en

IEEE: Copy

Noorollah Karimtabar, and Mohammad Javad Shayegan Fard, “An Extension of the Apriori Algorithm for Finding Frequent Items,” presented at the INTERNATIONAL CONFERENCE ON WEB RESEARCH. 2020, [Online]. Available: https://sid.ir/paper/949227/en

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans

No record.

Recommended Workshops