If you are looking to find outlier records based and then identify the file containing the records, K-means may be as good a place to start as any, but perhaps you need to join all the files together to train it first. One challenge with K-means and other clustering algorithms is that you have to start with $k$ but sometimes the value of $k$ is hard to determine because the good data can continue to break down into more discrete clusters as $k$ increases. This becomes a problem if your outliers have a high degree of uniqueness because you may need to increase $k$ to catch the outliers, but all you achieve is greater breakdown of the good records.
If you can identify the outliers, you should be able to create a training data set that could be used in a supervised technique like a
decision tree. It may take a little longer because you have to label your records, but this may give you a better result in the end.
If you are looking for combinations of records that should not exists in the same file or group of records and assuming you can identify records by some categorical identifier, e.g. transaction type, I would start with a probabilistic approach like
apriori, but you may want to check out
Naive Bayes based approach. There are some differences in these approaches, but, either one may produce decent results. More generally, I think the analysis you want to perform is $Association\ Rule\ Learning$. This type of technique can be used to derive a statistical probability for the combinations and then you can identify files that contain combinations that have a low probability of co-occurrence.