The Study of Feature Extraction and Concept Formation Method Based on the Relational Cohesiveness : The Verification was Made to Apply This Method to the Properties of Normal Alcohol Compounds as an Example

Abstract

The attributes to explain features of numeric dataset have been extracted depending on the similarity based on the distance between all pairs of data. The new extraction method was developed by paying attention to the symmetric relation over the entire data range that was called the relational cohesiveness in this article. This method was applied to investigate for the properties of normal alchohols, and it was found that some chemical features including the discrimination rule of the normal chain compounds were extracted by using both this method and the concept of the valence of the carbon and hydrogen. The method developed here can be applied to derive the knowledge from any numeric dataset in the database and measurement data, etc.