New Approach to Clustering Random Attributes
This addresses the challenge of clustering mixed data types for researchers in data analysis, but it appears incremental as it builds on existing encoding and factor analysis techniques.
The paper tackles the problem of clustering both numerical and nominal random attributes by proposing a method that encodes nominal attributes into numerical form for similarity analysis, and it demonstrates universality across sample datasets.
This paper proposes a new method for similarity analysis and, consequently, a new algorithm for clustering different types of random attributes, both numerical and nominal. However, in order for nominal attributes to be clustered, their values must be properly encoded. In the encoding process, nominal attributes obtain a new representation in numerical form. Only the numeric attributes can be subjected to factor analysis, which allows them to be clustered in terms of their similarity to factors. The proposed method was tested for several sample datasets. It was found that the proposed method is universal. On the one hand, the method allows clustering of numerical attributes. On the other hand, it provides the ability to cluster nominal attributes. It also allows simultaneous clustering of numerical attributes and numerically encoded nominal attributes.