similarity

I have a database with ~5,000 locality names, most of which are repetitions with typos, permutations, abreviations, etc. I would like to group them by similarity, to speed up further processing. The best would be to convert each variation into a "platonic form", and put two columns side by side, with the original and platonic forms. I've read about Multiple sequence alignment , but this seems to be mostly used in bioinformatics, for sequences of DNA/RNA/Peptides. I'm not sure it will work well with names of places. Anyone knows of a library that helps me to do it in R? Or which of the many

I have extracted two series MFCC coefficients from two around 30 second audio files consisting of the same speech content. The audio files are recorded at the same location from different sources. An estimation should be made whether the audio contains the same conversation or a different conversation. Currently I have tested a correlation calculation of the two Mfcc series but the result is not very reasonable. Are there best practices for this scenario? I had the same problem and the solution for it was to match the two arrays of MFCCs using the Dynamic Time Warping algorithm. After

In a MySQL database, I am attempting to find the most similar row across a number of numerical attributes. This problem is similar to this question but includes a flexible number of comparisons and a join table. Database The database consists of two tables. The first table, users, is what I'm trying to compare. id | self_ranking ---------------------------------- 1 | 9 2 | 3 3 | 2 The second table is a series of scores which the user gave to particular items. id | user_id | item_id | score ---------------------------------- 1 | 1 | 1 | 4 2 | 1 | 2 | 5 3 | 1 | 3 | 8 4 | 1 | 4 | 3 Task I want to

I want to use something like difflib.get_close_matches but instead of the most similar strings, I would like to obtain the indexes (i.e. position in the list). The indexes of the list are more flexible because one can relate the index to other data structures (related to the matched string). For example, instead of: >>> words = ['hello', 'Hallo', 'hi', 'house', 'key', 'screen', 'hallo', 'question', 'format'] >>> difflib.get_close_matches('Hello', words) ['hello', 'hallo', 'Hallo'] I would like: >>> difflib.get_close_matches('Hello', words) [0, 1, 6] There doesn't seem to exist a parameter to

I am new to programming.I have a pandas data frame in which two string columns are present. Data frame is like below: Col-1 Col-2 Update have a account Account account summary AccountDTH Cancel Balance Balance Summary Credit Card Update credit card Here i need to check the similarity of Col-2 elements with each element of Col-1. It Means i have to compare have a account with all the elements of Col-1 . Then find the top 3 similar one. Suppose the similarity scores are : Account(85),AccountDTH(80),Balance(60),Update(45),Credit Card(35) . Expected Output is: Col-2 Output have a account Account

Brief info I have 3 tables: Set: id name SetItem: set_id item_id position TempSet: id I have a function that generates new random combinations from Item table. Basically, always after successful generation, I create a new row in Set table, get it's id and add all item ids into SetItem table. Problem Every time before generating new combination I truncate the TempSet table, fill new item ids into this table and check for similarity percentage by comparing with previous combinations in SetItem table. if new combination similarity greater or equal to 30%, I need to prevent this combination and re

I have a large number of protein sequences in fasta format. I want to get the pair-wise sequence similarity score for each pairs of the proteins. Any package in R could be used to get the blast similarity score for protein sequences? As per Chase's suggestion, bioconductor is indeed the way to go and in particular the Biostrings package. To install the latter I would suggest installing the core bioconductor library as such: source("http://bioconductor.org/biocLite.R") biocLite() This way you will cover all dependencies. Now, to align 2 protein sequences or any two sequences for that matter you