Extracting partially ordered clusters from ordinal polytomous data.

摘要: In practical applications of knowledge space theory, states can be conceived as partially ordered clusters individuals. Existing extensions the theory to polytomous data lack methods for building “polytomous” structures. To this aim, an adaptation k-median clustering algorithm is proposed. It extension k-modes ordinal in which Hamming distance replaced by Manhattan distance, and central tendency measure median, rather than mode. The tested a series simulation studies application empirical data. Results show that there are theoretical reasons preferring algorithm, whenever responses items measured on scale. This because sensitive order levels, while not. Overall, seems promising data-driven procedure