Resolving data inconsistency in data integration

Web content mining describes the discovery of useful information from the Web contents / data / document s/information. (Data integration) A Data Inconsistency exists when two objects coming from different information sources are identified as versions of each other and some of the values of their corresponding attributes differ. The consistency is determined by using the WOWA criteria in fuzzy set. There are two important criteria are considered such a preferential weights and importance weights of web content. These weights are used to find the inconsistency and they are removed from the mixture. In this context, WOWA operator has the capability of assigning the preferential weights and important weights to the web content. It helps to obtain the inconsistent, by selecting the consistent data using the weights in the web content. The objective of this paper is to propose a FWOWA approach helps to discard the inconsistent data by avoiding the overfitting and improve the accuracy of the cluster. The inconsistency is determined by applying WOWA. By applying WOWA, the inconsistency is examined and it is removed from the Gaussian Mixture using (RPEM).