One way is to use Gower similarity coefficient which is a composite measure$^1$; it takes quantitative (such as rating scale), binary (such as present/absent) and nominal (such as worker/teacher/clerk) variables. Later Podani$^2$ added an option to take ordinal variables as well.
The coefficient is easily understood even without a formula; you compute the similarity value between the individuals by each variable, taking the type of the variable into account, and then average across all the variables. Usually, a program calculating Gower will allow you to weight variables, that is, their contribution, to the composite formula. However, proper weighting of variables of different type is a problem, no clear-cut guidelines exist, which makes Gower or other "composite" indices of proximity pull ones face.
The facets of Gower similarity ($GS$):
- When all variables are quantitative (interval) then the coefficient is the range-normalized Manhattan distance converted into similarity. Because of the normalization variables of different units may be safely used. You should not, however, forget about outliers. (You might also decide to normalize by another measure of spread than range.) Because of the said normalization by a statistic, such as range, which is sensitive to the composition of individuals in the dataset Gower similarity between some two individuals may change its value if you remove or add some other individuals in the data.
- When all variables are ordinal, then they are first ranked, and then Manhattan is computed, as above with quantitative variables, but with the special adjustment for ties.
- When all variables are binary (with an asymmetric significance of categories: "present" vs "absent" attribute) then the coefficient is the Jaccard matching coefficient (this coefficient treats when both individuals lack the attribute as neither match nor mismatch).
- When all variables are nominal (also including here dichotomous with symmetric significance: "this" vs "that") then the coefficient is the Dice matching coefficient that you obtain from your nominal variables if recode them into dummy variables (see this answer for more).
(It is easy to extend the list of types. For example, one could add a summand for count variables, using normalized chi-squared distance converted to similarity.)
The coefficient ranges between 0 and 1.
"Gower distance". Without ordinal variables present (i.e. w/o using the Podani's option) $\sqrt{1-GS}$ behaves as Euclidean distance, it fully supports euclidean space. But $1-GS$ is only metric (supports triangular inequality), not Euclidean. With ordinal variables present (using the Podani's option) $\sqrt{1-GS}$ is only metric, not Euclidean; and $1-GS$ isn't metric at all. See also.
With euclidean distances (distances supporting Euclidean space), virtually any classic clustering technique will do. Including K-means (if your K-means program can process distance matrices, of course) and including Ward's, centroid, median methods of Hierarchical clustering. Using K-means or other those methods based on Euclidean distance with non-euclidean still metric distance is heuristically admissible, perhaps. With non-metric distances, no such methods may be used.
The previous paragraph talks about if K-means or Ward's or such clustering is legal or not with Gower distance mathematically (geometrically). From the measurement-scale ("psychometric") point of view one should not compute mean or euclidean-distance deviation from it in any categorical (nominal, binary, as well as ordinal) data; therefore from this stance you just may not process Gower coefficient by K-means, Ward etc. This viewpoint warns that even if a Euclidean space is present it may be granulated, not smooth (see related).
If you want all the formulae and additional info on Gower similarity / distance, please read the description of my SPSS macro !gower; it's in the Word document found in collection "Various proximities" on my web-page.
$^1$ Gower J. C. A general coefficient of similarity and some of its properties // Biometrics, 1971, 27, 857-872
$^2$ Podani, J. Extending Gower’s general coefficient of similarity to ordinal characters // Taxon, 1999, 48, 331-340
Latitude and longitude are scale variables. Time is scale, too (I hope it is linear, not cyclic). Provider is nominal variable. I see two options:
- Use Two-step cluster analysis. This is the method of choice if you have many (thousands) of objects (nodes) to cluster. This method has a nice option to detect outliers automatically; aside from this it is quite coarse method.
- Use Hierarchical cluster analysis basing it on Gower coefficient (look here for links where you could compute it). This clustering is appropriate if the number of objects is, say, up to 500. You should choose among several agglomeration methods. With Gower coefficient, since it is not euclidean/metric, only average, single, complete methods should be considered consistent (but not Ward or centroid or median). You probably choose between average and complete (or try both), for single produces too oblong clusters.
There exist, of course, other clustering methods potentially apropriate (for example, a modification of K-Means that can take nominal variables), but I haven't use them, so can't recommend.
A good way to decide on the proper number of clusters is to use some internal clustering criterion, such as Silhouette statistic, cophenetic correlation, BIC, etc. (if you use SPSS, find macros to compute them on my web-page). In clustering, you produce and save a range of cluster solutions (say, from 20-cluster solution to 2-cluster solution) which are variables of cluster membership, and then check by one or more clustering criterions which of the solutions represent the most well-separated clusters - ideal solution is when density inside clusters is high and between them is low.
Best Answer
For binary attributes, the weight will not always be 1. "Hallucination" is supposed to be ignored altogether in that example.
This yields: $$ \frac{(1-(\frac{150-120}{150-110})+0+0+0+0}{1+1+1+0+1} = 1/16 $$