Manuscript Number : IJSRSET1841195
Analysis on Medical Data sets using Apriori Algorithm Based on Association Rules
Authors(2) :-Pamba Pravallika, K. Narendra
During the process of mining frequent item sets, when minimum support is little, the production of candidate sets is a kind of time-consuming and frequent operation in the mining algorithm. The APRIORI algorithm does not need to produce the candidate sets, the database which provides the frequent item set is compressed to a frequent pattern tree (or APRIORIl), and frequent item set is mining by using of APRIORIl. These algorithms considered as efficient because of their compact structure and also for less generation of candidates itemsets compare to Apriori and Apriori like algorithms. Therefore this paper aims to presents a basic Concepts of some of the algorithms (FP-Growth, COFI-Tree, CT-PRO) based upon the FP- Tree like structure for mining the frequent item sets along with their capabilities and comparisons. Data mining implementation on Medical data to generate rules and patterns using Frequent Pattern (FP)-Growth algorithm is the major concern of this research study. We presented in this paper how data mining can apply on Medical data.
Pamba Pravallika
Medical Data Mining, Association Mining, FP-Growth Algorithm, Frequent Data Sets
Publication Details
Published in :
Volume 4 | Issue 1 | January-February 2018 Article Preview
M.Tech Scholar , Department of CS, St.Marys Group Of Institutions Chebrolu(V&M),Guntur,
Jntu Kakinada Andhra Pradesh, India
K. Narendra
Assistant Professor, Department of Computer Science & Engineering, St.Marys Group of Institutions Chebrolu (V&M), Guntur, Jntu Kakinada Andhra Pradesh, India
Date of Publication :
2018-02-28
License: This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) :
717-722
Manuscript Number :
IJSRSET1841195
Publisher : Technoscience Academy
Journal URL :
https://ijsrset.com/IJSRSET1841195