AN EMPIRICAL STUDY OF VARIOUS FREQUENT ITEM SET MINING TECHNIQUES
Author(s):
Utkarsh Kumar Shrivastava
Keywords:
Data Mining, Frequent Pattern Mining, Support, Confidence, Apriori, DIC, Partitioning.
Abstract
Frequent item set mining has been a heart favorite theme for data mining researchers for over a decade. A large amount of literature has been dedicated to this research and tremendous progress has been made, ranging from efficient and scalable algorithms for frequent itemset mining in transaction databases to numerous research frontiers, such as sequential pattern mining, structured pattern mining, correlation mining, associative classification, and frequent pattern-based clustering, as well as their broad applications. In this paper, a literature review of various latest techniques for mining frequent items from a transaction data base are presented in critical manner,
Article Details
Unique Paper ID: 147526

Publication Volume & Issue: Volume 5, Issue 8

Page(s): 249 - 252
Article Preview & Download




Publish book

Go To Issue



Call For Paper

Volume 6 Issue 2

Last Date 25 July 2019


About Us

IJIRT.org enables door in research by providing high quality research articles in open access market.

Send us any query related to your research on editor@ijirt.org

Social Media

Google Verified Reviews

Contact Details

Telephone:8200 61 5067
Email: editor@ijirt.org
Website: ijirt.org

Policies