similarity

Let's say I have a dataset that look like this: {A:1, B:3, C:6, D:6} I also have a list of other sets to compare my specific set: {A:1, B:3, C:6, D:6}, {A:2, B:3, C:6, D:6}, {A:99, B:3, C:6, D:6}, {A:5, B:1, C:6, D:9}, {A:4, B:2, C:2, D:6} My entries could be visualized as a Table (with four columns, A, B, C, D, and E). How can I find the set with the most similarity? For this example, row 1 is a perfect match and row 2 is a close second, while row 3 is quite far away. I am thinking of calculating a simple delta, for example: Abs(a1 - a2) + Abs(b1 - b2) + etc and perhaps get a correlation

If we have a set of M words, and know the similarity of the meaning of each pair of words in advance (have a M x M matrix of similarities), which algorithm can we use to make one k-dimensional bit vector for each word, so that each pair of words can be compared just by comparing their vectors (e.g. getting the absolute difference of vectors)? I don't know how this particular problem is called. If I knew, it would be much easier to find among a bunch of algorithms with similar descriptions, which do something else. Additional observation: I think this algorithm would have to produce one, in

There are some surprisingly good image compare tools which find similar image even if it's not exactly the same (eg. change in size, wallpaper, brightness/contrast). I have some example applications here: Unique Filer 1.4 (shareware): https://web.archive.org/web/20010309014927/http://uniquefiler.com/ Fast Duplicate File Finder (Freeware): http://www.mindgems.com/products/Fast-Duplicate-File-Finder/Fast-Duplicate-File-Finder-About.htm Visual similarity duplicate image finder (payware): http://www.mindgems.com/products/VS-Duplicate-Image-Finder/VSDIF-About.htm Duplicate Checker (payware): http:/

I want to calculate the similarity between two list of words, for example : ['email','user','this','email','address','customer'] is similar to this list: ['email','mail','address','netmail'] I want to have a higher percentage of similarity than another list, for example: ['address','ip','network'] even if address exists in the list. Since you haven't really been able to demonstrate a crystal output, here is my best shot: list_A = ['email','user','this','email','address','customer'] list_B = ['email','mail','address','netmail'] In the above two list, we will find the cosine similarity between

I want to know the best way to rank sentences based on similarity from a set of documents. For e.g lets say, 1. There are 5 documents. 2. Each document contains many sentences. 3. Lets take Document 1 as primary, i.e output will contain sentences from this document. 4. Output should be list of sentences ranked in such a way that sentence with FIRST rank is the most similar sentence in all 5 documents, then 2nd then 3rd... Thanks in advance. I'll cover the basics of textual document matching... Most document similarity measures work on a word basis, rather than sentence structure. The first