Mining association rules at multiple levels helps in finding more specific and relevant knowledge. While computing the number of frequency of an item we need to scan the given database many times. So we used counting inference approach for finding frequent itemsets at each concept levels which reduce the number of scan. In this paper, we purpose a new algorithm LWFT which follow the topdown progressive deepening method and it is based on existing algorithms for finding multiple level association rules. This algorithm is efficient for finding frequent itemsets from large databases.

The premise of this paper is to use an efficient encoding scheme which will be used to encode high level concept hierarchy of a transactional table. This table will work as the base to generate multiple level association rules. These rules discovers the hidden knowledge align at higher level of abstraction. Therefore the numeric encoding of the concept hierarchy improves the time complexity and space complexity of task relevant data.

Everybody find ease and comfort by using their natural languages to communicate. There is large quantity of natural language material and for this reason there is the need of computer to involve in this process. Beside this people need to communicate with machines and people find natural languages natural. Although there are people who thinks, that only people can effectively use natural languages and thus it is inappropriate to bring computers into this arena, there is already evidence, that programs that manipulate language in various ways can be useful. In this paper, we are extracting information for user based natural language query on the health domain. Information retrieval through such Q/A systems is important sources to help physicians make decisions in patient treatment and as a result, to enhance the quality of patient care by retrieving a vast amount of information in response to a specific user query. KeywordsAlgorithm design for input statement understanding module, Results on developer side.