When-to-use-what data mining algorithms:

 

Segmentation algorithms	A segmentation algorithm divides data into groups or clusters or items that have similar properties.	Customer segmentation based on service request feature set is a very common application of this algorithm. It helps prioritize the response to certain customers.
Association algorithms	This is used for finding correlations between different attributes in a data set	Association data mining allows these users to see helpful messages such as “users who opened a ticket for this problem type also opened a ticket for this other problem type”
Sequence Analysis Algorithms	This is used for finding groups via paths in sequences. A Sequence Clustering algorithm is like a clustering algorithm mentioned above but instead of finding groups based on similar attributes, it finds groups based on similar paths in a sequence.  A sequence is a series of events. For example, a series of web clicks by a user is a sequence. It can be also be compared to the IDs of any sortable data maintained in a separate table. Usually, there is support for a sequence column. The sequence data has a nested table that contains a sequence ID which can be any sortable data type.	This is very useful to find sequences of service requests opened across customers. Generally, a network failure could result in a database connection failure which could lead to an application failure. This sort of sequence determination in a data driven manner helps find new sequences and target them actively even suggesting the same to the customers who open the request so that they can be better prepared. Sequence Analysis also helps with ChatBot experience for IT users as described here.