Abstract
A fuzzy neighborhood model to analyze text data is proposed. This method can represent a sequencial structure in a set of texts, while traditional methods like the vector space model cannot as it simply counts the number of words in a text. Moreover fuzzy neighborhood model is a generalization of the vector space model and fuzzy equivalence relations. An advantage of this model is that it provides a positive definite kernel for data analysis. Accordingly we apply the present model to text analysis using kernel c-means clustering and kernel principal component analysis. Two examples of analysis of newspaper articles and medical incident reports are shown.
