Extrication of Apriori Algorithm using Association Rules on Medical Data sets

Authors

  • Anusha Viswanadapalli  Assistant Professor, Department of Computer Science & Engineering, Sri Mittapalli Institute of Technology, JNTU Kakinada, Andhra Pradesh, India
  • Praveen Kumar Nelapati  Assistant Professor, Department of Computer Science & Engineering, NRI Institute of Technology, JNTU, Kakinada, Andhra Pradesh, India

DOI:

https://doi.org/10.32628/IJSRSET19627

Keywords:

MEDICAL Data Mining, Association Mining, APRIORI-Growth Algorithm, Frequent Data Sets

Abstract

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 growth 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 APRIORI tree), and frequent item set is mining by using of APRIORI tree. These algorithms considered as efficient because of their compact structure and also for less generation of candidates item sets compare to Apriori and Apriori like algorithms. Therefore this paper aims to presents a basic Concepts of some of the algorithms (APRIORI-Growth, COFI-Tree, CT-PRO) based upon the APRIORI- 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 (APRIORI)-Growth algorithm is the major concern of this research study. We presented in this paper how data mining can apply on MEDICAL data.

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International Journal of Scientific Research in Science, Engineering and Technology

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Published

2019-04-30

Issue

Volume 6, Issue 3, May-June-2019

Section

Research Articles

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Anusha Viswanadapalli, Praveen Kumar Nelapati "Extrication of Apriori Algorithm using Association Rules on Medical Data sets" International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 6, Issue 3, pp.107-112, May-June-2019. Available at doi : https://doi.org/10.32628/IJSRSET19627