Measuring Proximity between Heterogeneous Data

Measuring proximity (or similarity) underlies many algorithms in information processing (by humans or indeed animals, or by machines). When the data involved are heterogeneous this issue is further complicated. This paper discusses some of the difficulties of measuring similarity between heterogeneous data and possible directions for addressing these difficulties using computational intelligence/fuzzy set based techniques. An important concept underlying the discussion is that of consistent meaning of proximity.