Emancipation of FP Growth Algorithm using Association Rules on Spatial Data Sets

Authors(2) :-Sudheer Kumar Muppalla, Raveendra Reddy Enumula

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 FP 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 FP tree), and frequent item set is mining by using of FP tree. 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 spatial 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 spatial data.

Spatial Data Mining, Association Mining, FP-Growth Algorithm, Frequent data sets

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Publication Details

Published in : Volume 3 | Issue 5 | July-August 2017
Date of Publication : 2017-08-31
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 604-608
Manuscript Number : IJSRSET1734102
Publisher : Technoscience Academy

Print ISSN : 2395-1990, Online ISSN : 2394-4099

Cite This Article :

Sudheer Kumar Muppalla, Raveendra Reddy Enumula, " Emancipation of FP Growth Algorithm using Association Rules on Spatial Data Sets, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 3, Issue 5, pp.604-608, July-August-2017.
Journal URL : http://ijsrset.com/IJSRSET1734102

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