Attribute Correction - Data Cleaning Using Association Rule and Clustering Methods

Abstract

Data cleaning, also called data cleansing or scrubbing, deals with detecting and removing errors and inconsistencies from data in order to improve the quality of data. Data quality problems are present in single data collections, such as files and databases,. When multiple data sources need to be integrated, e.g., in data warehouses, federated database systems or global web-based information systems, the need for data cleaning increases significantly. Data cleaning is the necessary condition of knowledge discovery and data warehouse building. In this paper two algorithms are designed using data mining technique to correct the attribute without external reference. One is Context-dependent attribute correction and another is Context-independent attribute correction.