- Attributes, Data cleaning, Clustering
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 timecalculated analysis. Clustering is one of the basic techniques often used in analyzing data sets. The Hierarchical and DBSCAN clustering Advantage and disadvantage also discussed.