The modern world all of them using internet and searching data according to their needs but the result of the data is not accurate some added impurities added in the data that impurities has removing is the important concept of the data mining in the dataset. In our new approach we are going to eliminate the impurities in the data. Those impurities are called as the outliers. We are going to use the Advance Segment Intelligence Chronicle Data Detection algorithm to eliminate the outliers in the searching data’s and give the best result to the user’s need in this methodology the data searching is the best performance and it going give the exact result to the user. During the process of cluster the data has comes like bunch of the data that data we are going to eliminate the impurities. Basically the clustering two type of clustering first one data mining and spatial mining these are the two types. In that we are going to use the data mining and eliminate the impurities like fraud detection and removing the unwanted data’s. Here we are going to use the three values to find the outliers. First value tendency this tendency value base on the local behaviour and second value has used in the kernel k clustering the type of local outlier factor based method and third value has the threshold. It used to give the better performance of the data clustering.