Title: Data Analysis Enhancement With Noise Removal using HCLEANER Method
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Title: |
Data Analysis Enhancement With Noise Removal using HCLEANER Method |
Author Name: |
Miss Pradnya P. Sondwale |
Abstract: |
Abstract -
The important goal of data cleaning method is removing object that are noise which impedes most types of data analysis.The main focus of is on removing the noise which is the product of low level data errors that result from an imperfect data collection process but the data object which are inapplicable or weakly related can also significantly hinder data analysis.Thus to enhance the data analysis as much as possible,these object should also be considered as noise at least with respect to underlying analysis.It contains four methods planned for noise removal .Three of these methods are traditional which are based on outlier detection technique: distance- based,approach- based and an clustering-based on LOF(Local Outlier Factor) of an object.The another technique is a new method which is a hyperclique based data cleaner (HCleaner).These technique is evaluated in terms of data analysis ,specifically clustering and association analysis. The experimental results show that all of these methods are providing better clustering performance and higher quality association patterns as the amount of noise being removed is increases, although HCleaner generally leads to better clustering performance and higher quality associations than the other three methods for binary data.
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