This article proposes the modified KNN (K Nearest Neighbor) algorithm which receives a string vector as its input data and is applied to the index optimization. The results from applying the string vector based algorithms to the text categorizations were successful in previous works, and the index optimization is able to be viewed into a classification task where each word is classified into expansion, inclusion, and removal. In the proposed system, each word in the given text is classified into one of the three categories by the proposed KNN algorithm, associates words are added to ones which are classified into expansion, and ones which are classified into inclusion are kept by themselves without adding any word. The proposed KNN version is empirically validated as thebetter approach in deciding the importance level of words in news articles and opinions. We need to define and characterize mathematically more operations on string vectors for modifying moreadvanced machine learning algorithms.