Keywords are used in different approaches. In text editing and DBMS (Data Base Management System), a keyword is used to find certain records. In programming languages, a keyword is the reserved word in a program since it has a meaning. It is also used to search the relevant web pages through a search engine. In spatial database having a set of objects, each object is associated with a keyword such as hotels, restaurants, etc. Here the issue is closest keyword cover search also called as keyword cover, which covers a set of query keyword and minimum inter object distance. At present days we are giving the importance of keyword rating to make a better decision. Later, generic version of the keyword cover called best keyword cover, which the objects cover the inter object distance and keyword rating. Baseline algorithm simulates closest keywords search, combining objects from query keyword to generate a candidate key. In baseline algorithm, the performance decreases because, as the number of query keyword increases the candidates increases, and to overcome this drawback, a scalable algorithm called Keyword Nearest Neighbour Expansion (K-NNE) variation was introduced with previous approach. The keyword Nearest Neighbour Expansion (K-NNE) gradually decreases the candidate key. In previous work with minimal tree cover, the query keyword in future finds the sub graph rather than minimal tree, which is more informative to the users. Nodes in a tree are close to each other, and the other nodes far away from each other has a weak relationship on content nodes in a tree. All keywords having same importance, ie, the result containing strong relationship that is the shortest distance between each pair of nodes selected over weak relationship. Tree base method content and non-content nodes in the tree during the results, hundreds and thousands of nodes in input graph have high time and memory complexity.