Analysis on Medical Data sets using Apriori Algorithm Based on Association Rules

Authors

  • Pamba Pravallika  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

Keywords:

Medical Data Mining, Association Mining, FP-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 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.

References

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International Journal of Scientific Research in Science, Engineering and Technology

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Published

2018-02-28

Issue

Volume 4, Issue 1, January-February-2018

Section

Research Articles

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How to Cite

[1]
Pamba Pravallika, K. Narendra, " Analysis on Medical Data sets using Apriori Algorithm Based on Association Rules , International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 4, Issue 1, pp.717-722, January-February-2018.