Term weighting based class indexes using space density for Al-Qur'an relevant meaning ranking

摘要

Nowadays information retrieval based on specific queries is already used in computer system. One of the popular methods is document ranking using Vector Space Model (SVM) based on TF.IDF term-weighting. In this paper TF.IDF.ICSδF term-weighting based class-indexing is proposed, afterward comparing its effectiveness to TF.IDF and TF.IDF.ICF term weighting. Each method is investigated through Al-Qur'an dataset. Al-Qur'an consist many verses, each verse of the Al-Qur'an is a single document which is ranked based on user query. The experimental show that the proposed method can be implemented on document ranking and the performance is better than previous methods with accurate value 93%.