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I have a list of items that represent cells, now i query each cell value to get a set of possible categories that this cell might belong to, these categories are weighted. For Example:

Microsoft is: {Software=83.543266, Video Game Platform=132.297455, Degree=71.624733, Organization=208.17038, Programming Language=66.998901, File Format=67.159828}

apple is: {Physicist=50.008327, Organism Classification=145.239532, Video Game Platform=86.5653, Computer=60.457172, Organization=199.53299, Record label=53.477039}

Banana is: {Musical Group=27.214769, Organism Classification=136.063583, Writer=27.566528, Musical instrument=29.285137, Organization=36.962833, TV Program=32.693085}

Blackberry is: {Software=23.69421, Organism Classification=64.572891, Operating System=90.257011, City/Town/Village=16.836142, Computer=34.484653}

The weights that i get for each result are independent at cell level, so Microsoft having a very high confidence as an organization does not relate to the confidence score of Apple organization confidence.

Now i have two problems, the first is to compare two different cells and compute a similarity score, for example how similar apple is to Microsoft having these possible categories for each.

Second, i want to deduce a global common category for the column that represents these cells, for example if i run a simple algorithm that will calculate the number of categories occurrences and the average of their confidences will not be accurate.

I will appreciate any help.

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  • $\begingroup$ What would the point of the similarity score be? For example, it's entirely sure that "Sun" is a large IT corporation, just as Microsoft is, but it's surely likely to have a much higher score for "celestial object", so your similarity may be quite low... $\endgroup$ – naught101 Jun 7 '12 at 2:35
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For the first question, a cosine of the angle between the two (aligned) category vectors of two given items is often used as a measure of similarity.

I don't quite understand the second question. Are you referring to some sort of dimensionality reduction / latent factor approach applied to the category vectors?

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    $\begingroup$ This is something like what I was thinking too. Probably should add something about the computation of cosine ($cos\theta=\frac {a\cdot b} {\left\|a\right\|\left\|b\right\|} $)... $\endgroup$ – naught101 Jun 7 '12 at 2:31

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