All Categories


Pages


STUDY ON RETAIL MARKETING SALE DATA USING R SOFTWARE DATA CLEANING AND CLUSTERING ALGORITHMS

Abstract: Nowadays, data cleaning solutions are very essential for the large amount of data handling users in an industry and others. The data were collected from Retail Marketing sale data in terms of the mentioned attributes. Normally, data cleaning, deals with detecting, outlier detection, removing errors and inconsistencies from data in order to improve the quality of data. There are number of frameworks to handle the noisy data and inconsistencies in the market. While traditional data integration problems can deal with single data sources at instance level. The Hierarchical clusters and DBSCAN clusters were grouped with related similarities, analysis and Time taken to build model in different cluster mode was experimented using WEKA tool. It also focuses on different input retail marketing data by time calculated analysis. Clustering is one of the basic techniques often used in analyzing data sets. The Hierarchical and DBSCAN clustering Advantage and disadvantage also discussed.

Index Terms—Attributes, Data cleaning, Clustering

Click Here for Full Paper Access




About the Author

Administrator

N.MARUDACHALAM & L.RAMESH


Comments

Cecelia SeligSeptember 27, 2017 Reply

I blog quite often and I truly appreciate
your content. This great article has really peaked my interest.
I am going to take a note of your website and keep checking for new details about
once a week. I subscribed to your Feed too.


Your Response



Most Viewed - All Categories

free
web stats