Human-microbiome-relations extraction is important for analyzing the effects on human gut microbiome from the difference of human attributes such as country, sex, age and so on. Human gut microbiome, a set of bacteria, provides various pathological and biological impacts on a hosting human body system. This paper presents a new analytical method for data resources that are difficult to understand such as human gut microbiome, by extracting the unknown relations with other adjunct metadata (e.g. human attributes data) with context-dependent clustering and semantic analysis. This method realizes the significant bacterial components acquisition for categorizing human attributes. The most important feature of our method is to analyze the unknown relations of human-microbiome with or without a correlation between a human attribute and bacteria that is found by related studies in bacteriology. With this method, an analyst is able to grasp the overview of bacteria data clustered by several clustering algorithms (k-means clustering / hierarchical clustering) using bacteria data selected by human attributes as a set of context. In addition, even without an association between a human attribute and bacteria as heuristic knowledge, an analyst is able to extract human-microbiome-relations focusing on a number of bacteria selected from all bacteria combinations by one-way analysis of variance (ANOVA) and our original criteria called the 'degree of separation' of clustering. This paper also presents an experimental study about human-microbiome-relations extraction and the experimental results that show the feasibility and effectiveness of this method.