Enhancing web service clustering using Length Feature Weight Method for service description document vector space representation
作者：
Highlights：
• Extracted features from web services and NLP is applied for preprocessing.
• Proposed Length Feature Weight Method for vector form of preprocessed services.
• Applied K-Mean clustering on the vector representation of web service documents.
• Achieved better clustering performance measured using standard measurement criteria.
摘要
•Extracted features from web services and NLP is applied for preprocessing.•Proposed Length Feature Weight Method for vector form of preprocessed services.•Applied K-Mean clustering on the vector representation of web service documents.•Achieved better clustering performance measured using standard measurement criteria.
论文关键词：Web Service Clustering,Web Service Description Language (WSDL),Term Frequency – Inverse Document Frequency (TF-IDF),Length Feature Weight (LFW),K-Means clustering
论文评审过程：Received 3 July 2019, Revised 18 June 2020, Accepted 18 June 2020, Available online 4 July 2020, Version of Record 13 July 2020.
论文官网地址：https://doi.org/10.1016/j.eswa.2020.113682
