Web services playing a vital role in the World Wide Web and generates huge amount of information across various domains of internet. Due to this evolution data in the form of articles, reports, digital galleries and web data of companies were increased everyday. To handle the huge volume of data each and every day, automatic query classification based on internet is more significant method. Research and development community has developed various techniques for the web services discovery, where it offers the mandated data for the improvement method. With respect to the literature survey, most of the researchers are concentrating to provide the efficient web service discovery. The amount of data that is available in the web is keeps on increasing and also it is used to differentiate the services, explanation and work of art. In order to achieve this method, machine learning algorithm is applied extensively for domain categorization. Various machine learning algorithm like KNN is applied for web service discovery. The systems are effectively learning the input and evaluate the performance accuracy with the given datasets. This paper, proposes an improved fuzzy with KNN algorithm for effective web service classification. This is used to increase an outcome in the form of accuracy and performance measures.

Web服务在万维网中起着至关重要的作用，并在Internet的各个领域中生成大量信息。由于这种以文章，报告，数字画廊和网络数据形式出现的数据每天都在增加。为了每天处理大量数据，基于Internet的自动查询分类是更为重要的方法。研究和开发社区已经开发了各种用于Web服务发现的技术，其中提供了用于改进方法的授权数据。关于文献调查，大多数研究人员正在致力于提供有效的Web服务发现。网络上可用的数据量不断增加，也用于区分服务，说明和艺术品。为了实现该方法，机器学习算法被广泛应用于领域分类。诸如KNN的各种机器学习算法被应用于Web服务发现。系统正在有效学习输入，并使用给定的数据集评估性能准确性。本文提出了一种改进的KNN模糊算法，用于有效的Web服务分类。这用于以准确性和性能度量的形式增加结果。提出了一种改进的KNN模糊算法，用于有效的Web服务分类。这用于以准确性和性能度量的形式增加结果。提出了一种改进的KNN模糊算法，用于有效的Web服务分类。这用于以准确性和性能度量的形式增加结果。