Open AccessComparison between the Naïve Bayes and Hierarchical Clustering to Classify The Global Landslide Catalog for the Prediction of the Landslide.
Author(s) -
Pratima Verma,
Charu Negi,
Nisha Chandran S,
Narendra Singh Bohra
Publication year - 2020
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.l1008.10812s319
Subject(s) - landslide , hierarchical clustering , cluster analysis , bayes' theorem , computer science , artificial intelligence , naive bayes classifier , machine learning , consensus clustering , data mining , bayesian probability , geology , fuzzy clustering , support vector machine , canopy clustering algorithm , geotechnical engineering
Machine Learning has been used since long toidentify the features of a given datasets that are important for theprediction. Landslides are complex events taking place in thevarious regions of the world. It is the movement of the debris, soilor rocks from an upper plane in downward direction.Identification of the features that are used for the Landslideinvolves consideration of various categories of parameters.Present paper studies about the performance comparisonbetween a supervised algorithm Naïve Bayes and unsupervisedalgorithm Hierarchical Clustering. Naïve Bayes is a nonparametric supervised algorithm that can be used for theforecasting purposes in the field of Agriculture, Economics,Aviation etc, whereas Hierarchical Clustering is used to partitionthe available instances of a dataset into optimal homogeneousgroups on the basis of the similarities between the datapoints.The present paper draws a comparison between the accuracy ofthe Naïve Bayes and Hierarchical Clustering for the prediction ofthe Landslide dataset. The dataset used is the Global LandslideCatalog that has important parameters like date, locationcoordinates, country, trigger of the event, continent etc. Beforethe implementation of both the algorithms, reduction of theparameters is carried out using subset evaluation of theparameters and considering only the most important.