i have a data set like the following
1=> aa,ser,sdf,gg,er,we <br/>
2=> gg,aa,uy,de,or,qq<br/>
3=> aa,er,we,uy<br/>
4=> oo,aa,gg,tr,dw<br/>
5=> iu,gg,re,de,ser<br/>
likewise there are about 1000 transactions.
i want to fine what items found more frequently with
"aa", "aa" and "gg", "oo"
etc...
whenever i name an item, other items that are frequently used with it should be displayed. What type of algorithm/algorithms is suitable to be used in this sort of a situation?
Split it all into one big two-column table:
num,wrd
===,===
1,aa
1,er
1,gg
1,sdf
1,ser
1,we
2,aa
2,dd
...
5,re
5,ser
From there, it's easier to query out what you want. For example,
select wrd, count(*) from words group by wrd order by count(*) desc;
Association rule learning could be a simple and fast option.
There are many options depending on how you want to tune the solution. Check this survey on the topic.
Related
A bioinformatics programming question. In R, I have a classic speciesA-to-speciesB gene symbol conversion, in this example from mouse to human, which I'm performing using biomaRt, and specifically the getLDS function.
x<-c("Lbp","Ndufv3","Ggt1")
require(biomaRt)
convert<-function(x){
human=useMart("ensembl",dataset="hsapiens_gene_ensembl")
mouse=useMart("ensembl",dataset="mmusculus_gene_ensembl")
newgenes=getLDS(
attributes="mgi_symbol",
filters="mgi_symbol",
values=x,
mart=mouse,
attributesL="hgnc_symbol",
martL=human,
uniqueRows=TRUE
)
humanx<-unique(newgenes)
return(humanx)
}
conversion<-convert(x)
However, I would like to obtain ALL ids present in the linked database: in other words, all mouse/human pairs (in this example). Something to tell the parameter value in the getLDS function to retrieve all ids, not just those specified in the x variable. I am talking about a full map, tens of thousands of lines long, specifying all orthologous relationships between symbols of the two databases.
Any ideas or workarounds? Thanks a lot!
I believe a workaround could be retrieving all IDs from the Biomart database itself, here: https://www.ensembl.org/biomart/martview/
Click on choose database -> Ensembl Genes
Choose dataset -> your selected species (e.g. Mouse genes)
Click on Results -> Check "Unique results only" -> Go
Profit
The list retrieved here has currently 53605 ids, which is, I believe, what you need.
Enjoy!
I am working with dplyr and the dbplyr package to interface with my database. I have a table with millions of records. I also have a list of values that correspond to the key in that same table I wish to filter. Normally I would do something like this to filter the table.
library(ROracle)
# connect info omitted
con <- dbConnect(...)
# df with values - my_values
con %>% tbl('MY_TABLE') %>% filter(FIELD %in% my_values$FIELD)
However, that my_values object contains over 500K entries (hence why I don't provide actual data here). This is clearly not efficient when they will basically be put in an IN statement (It essentially hangs). Normally if I was writing SQL, I would create a temporary table and write a WHERE EXISTS clause. But in this instance, I don't have write privileges.
How can I make this query more efficient in R?
Note sure whether this will help, but a few suggestions:
Find other criteria for filtering. For example, if my_values$FIELD is consecutive or the list of values can be inferred by some other columns, you can seek help from the between filter: filter(between(FIELD, a, b))?
Divide and conquer. Split my_values into small batches, make queries for each batch, then combine the results. This may take a while, but should be stable and worth the wait.
Looking at your restrictions, I would approach it similar to how Polor Beer suggested, but I would send one db command per value using purrr::map and then use dplyr::bindrows() at the end. This way you'll have a nice piped code that will adapt if your list changes. Not ideal, but unless you're willing to write a SQL table variable manually, not sure of any other solutions.
I'm attempting to clean up a database that, over the years, had acquired many duplicate records, with slightly different names. For example, in the companies table, there are names like "Some Company Limited" and "SOME COMPANY LTD!".
My plan was to export the offending tables into R, convert names to lower case, replace common synonyms (like "limited" -> "ltd"), strip out non-alphabetic characters and then use agrep to see what looks similar.
My first problem is that agrep only accepts a single pattern to match, and looping over every company name to match against the others is slow. (Some tables to be cleaned will have tens, possibly hundreds of thousands of names to check.)
I've very briefly looked at the tm package (JSS article), and it seems very powerful but geared towards analysing big chunks of text, rather than just names.
I have a few related questions:
Is the tm package appropriate for this sort of task?
Is there a faster alternative to agrep? (Said function uses the
Levenshtein edit distance which is anecdotally slow.)
Are there other suitable tools in R, apart from agrep and tm?
Should I even be doing this in R, or should this sort of thing be
done directly in the database? (It's an Access database, so I'd
rather avoid touching it if possible.)
If you're just doing small batches that are relatively well-formed, then the compare.linkage() or compare.dedup() functions in the RecordLinkage package should be a great starting point. But if you have big batches, then you might have to do some more tinkering.
I use the functions jarowinkler(), levenshteinSim(), and soundex() in RecordLinkage to write my own function that use my own weighting scheme (also, as it is, you can't use soundex() for big data sets with RecordLinkage).
If I have two lists of names that I want to match ("record link"), then I typically convert both to lower case and remove all punctuation. To take care of "Limited" versus "LTD" I typically create another vector of the first word from each list, which allows extra weighting on the first word. If I think that one list may contain acronyms (maybe ATT or IBM) then I'll acronym-ize the other list. For each list I end up with a data frame of strings that I would like to compare that I write as separate tables in a MySQL database.
So that I don't end up with too many candidates, I LEFT OUTER JOIN these two tables on something that has to match between the two lists (maybe that's the first three letters in each list or the first three letters and the first three letters in the acronym). Then I calculate match scores using the above functions.
You still have to do a lot of manual inspection, but you can sort on the score to quickly rule out non-matches.
Maybe google refine could help. It looks maybe more fitted if you have lots of exceptions and you don't know them all yet.
What you're doing is called record linkage, and it's been a huge field of research over many decades already. Luckily for you, there's a whole bunch of tools out there that are ready-made for this sort of thing. Basically, you can point them at your database, set up some cleaning and comparators (like Levenshtein or Jaro-Winkler or ...), and they'll go off and do the job for you.
These tools generally have features in place to solve the performance issues, so that even though Levenshtein is slow they can run fast because most record pairs never get compared at all.
The Wikipedia link above has links to a number of record linkage tools you can use. I've personally written one called Duke in Java, which I've used successfully for exactly this. If you want something big and expensive you can buy a Master Data Management tool.
In your case probably something like edit-distance calculation would work, but if you need to find near duplicates in larger text based documents, you can try
http://www.softcorporation.com/products/neardup/
I'm attempting to clean up a database that, over the years, had acquired many duplicate records, with slightly different names. For example, in the companies table, there are names like "Some Company Limited" and "SOME COMPANY LTD!".
My plan was to export the offending tables into R, convert names to lower case, replace common synonyms (like "limited" -> "ltd"), strip out non-alphabetic characters and then use agrep to see what looks similar.
My first problem is that agrep only accepts a single pattern to match, and looping over every company name to match against the others is slow. (Some tables to be cleaned will have tens, possibly hundreds of thousands of names to check.)
I've very briefly looked at the tm package (JSS article), and it seems very powerful but geared towards analysing big chunks of text, rather than just names.
I have a few related questions:
Is the tm package appropriate for this sort of task?
Is there a faster alternative to agrep? (Said function uses the
Levenshtein edit distance which is anecdotally slow.)
Are there other suitable tools in R, apart from agrep and tm?
Should I even be doing this in R, or should this sort of thing be
done directly in the database? (It's an Access database, so I'd
rather avoid touching it if possible.)
If you're just doing small batches that are relatively well-formed, then the compare.linkage() or compare.dedup() functions in the RecordLinkage package should be a great starting point. But if you have big batches, then you might have to do some more tinkering.
I use the functions jarowinkler(), levenshteinSim(), and soundex() in RecordLinkage to write my own function that use my own weighting scheme (also, as it is, you can't use soundex() for big data sets with RecordLinkage).
If I have two lists of names that I want to match ("record link"), then I typically convert both to lower case and remove all punctuation. To take care of "Limited" versus "LTD" I typically create another vector of the first word from each list, which allows extra weighting on the first word. If I think that one list may contain acronyms (maybe ATT or IBM) then I'll acronym-ize the other list. For each list I end up with a data frame of strings that I would like to compare that I write as separate tables in a MySQL database.
So that I don't end up with too many candidates, I LEFT OUTER JOIN these two tables on something that has to match between the two lists (maybe that's the first three letters in each list or the first three letters and the first three letters in the acronym). Then I calculate match scores using the above functions.
You still have to do a lot of manual inspection, but you can sort on the score to quickly rule out non-matches.
Maybe google refine could help. It looks maybe more fitted if you have lots of exceptions and you don't know them all yet.
What you're doing is called record linkage, and it's been a huge field of research over many decades already. Luckily for you, there's a whole bunch of tools out there that are ready-made for this sort of thing. Basically, you can point them at your database, set up some cleaning and comparators (like Levenshtein or Jaro-Winkler or ...), and they'll go off and do the job for you.
These tools generally have features in place to solve the performance issues, so that even though Levenshtein is slow they can run fast because most record pairs never get compared at all.
The Wikipedia link above has links to a number of record linkage tools you can use. I've personally written one called Duke in Java, which I've used successfully for exactly this. If you want something big and expensive you can buy a Master Data Management tool.
In your case probably something like edit-distance calculation would work, but if you need to find near duplicates in larger text based documents, you can try
http://www.softcorporation.com/products/neardup/
I have two tables, one contains a list of items which is called watch_list with some important attributes and the other is just a list of prices which is called price_history. What I would like to do is group together 10 of the lowest prices into a single column with a group_concat operation and then create a row with item attributes from watch_list along with the 10 lowest prices for each item in watch_list. First I tried joins but then I realized that the operations where happening in the wrong order so there was no way I could get the desired result with a join operation. Then I tried the obvious thing and just queried the price_history for every row in the watch_list and just glued everything together in the host environment which worked but seemed very inefficient. Now I have the following query which looks like it should work but it's not giving me the results that I want. I would like to know what is wrong with the following statement:
select w.asin,w.title,
(select group_concat(lowest_used_price) from price_history as p
where p.asin=w.asin limit 10)
as lowest_used
from watch_list as w
Basically I want the limit operation to happen before group_concat does anything but I can't think of a sql statement that will do that.
Figured it out, as somebody once said "All problems in computer science can be solved by another level of indirection." and in this case an extra select subquery did the trick:
select w.asin,w.title,
(select group_concat(lowest_used_price)
from (select lowest_used_price from price_history as p
where p.asin=w.asin limit 10)) as lowest_used
from watch_list as w