Text mining of machine logs to find correlation between errors in R I've with me 50 MB data from a machine consisting of event logs such as device status, warning and error. I wish to perform text mining on the same to find correlation between errors i.e. one error could trigger another in future and take a prescriptive action on it.
I've used hierarchical clustering in R to generate a dendrogram but the result couldn't yeild expected insight. Hence, I've planned to perform Apriori Algorithm on the same. 
My queries are:


*

*Is Hierarchical Clustering suggested to find such correlations?

*Is Apriori an apt algorithm in such situation and why? Or, is there any other approach to solve this problem?


The logs that I've looks like this :

 A: Apriori is one of the algorithms to solve Association rule learning a.k.a. Frequent Pattern Mining.
The problem is defined with sets of items in transactions. Within a single transaction, is there a set of items $A$ that will often tell us about with a second set of items $B$. First a minimum support is given by the user and is defined as the frequency of both $A$ and $B$ occurring in the same "transaction" over all transactions, i.e. $support=P(A \cup B)$. Next, the user defines a minimum confidence which is defined $confidence = P(B|A) = \frac{support(A \cup B)}{support(A)}$.
In this case, consider grouping the events from the logs over a period of time into a single transaction. Define the minimum support and minimum confidence, then Apriori will find which sets of events satisfy those minimums.
This usually returns many sets of items and usually a second metric is calculated to determine the best of these. Lift is the most common if these, but also consider these other association metrics
A: I think you should go for Apriori Algorithm
