Amplification Misplaced Answers to Crowd DB Using Fuzzylogic and K-Means Clustering Algorithm

Due to the fact that existing database systems are increasingly more difficult to use, improving the quality and the usability of database systems has gained tremendous momentum over the last few years. Data clustering is a process of arranging data into groups or a technique for classifying a mountain of information into some manageable meaningful piles. The goal of clustering is to partitions a dataset into several groups such that the similarity within a group is better than among groups. K- means is one of the basic clustering algorithm which is commonly used in several applications, but it is computationally time consuming and the quality of the resulting clusters heavily depends on the selection of initial centroids. We can remove first limitation using the Enhanced K- Means algorithm. This paper represents the comparison analysis of basic K-Means clustering algorithm and Enhanced K- Means clustering algorithm which shows Enhanced K-Means algorithm more effective and efficient than Basic K-means algorithm. we use the query-refinement method. That is, given as inputs the original top-k SQL query and a set of missing tuples, our algorithms return to the user a refined query that includes both the missing tuples and the original query results. Case studies and experimental results show that our algorithms are able to return high quality explanations efficiently.