Applications of Data Mining to Reward points collection service  

Continuation of use cases:    

Collaborative filtering can be applied via Item-based filtering as well. This is a different use case from the earlier cited for user-based filtering in that the item-based filtering avoids the divulging of users in the participant group and instead focuses on item similarity from a lookup table which makes it fast albeit storage expensive. In both cases, the similarity scores are computed but this approach allows us to answer the question whether the set of grants are like others which helps us rank them. This is useful for sparse data set which is typical for the matrix of appreciation across users. 

Sequence clustering provides insights into the activities that generated the appreciation because it determines patterns across users and grants by finding paths in sequence.  A sequence is a series of events such as a set of appreciations in the form of reward point grants. This kind of sequence analysis helps us understand the activities that were most popular for appreciation purposes between employees and target those actively on other forums. Sequence clustering is a data driven approach. It helps with determining sequences from existing appreciation activities. 

Machine learning techniques form an altogether separate category of their own. The traditional data mining methods used clustering and statistics which are relevant to machine learning, but we did not include the neural networks with data mining, and we call it out with others in this category. Machine learning is very helpful to inform users about their activities that generate the most appreciation and the changing of these activities depending on the audience. It can also detect fraud in the employee appreciations which may be of interest to employers. For example, Feedzai uses real-time behavioral profiling as well as historical profiling that has been proven to detect 61% more fraud than earlier. Discovering groups, searching and ranking are a few more examples. 

Regions of interest are used to determine space and time focus on appreciation activity. This is helpful to detect events that would have otherwise gone unnoticed as a flurry of activities on the reward points table. Together with the classifier and this regressor, the latent event and awardees can be detected thus eliminating the need to hold formal events and determine winners.

Conclusion: There are several algorithms in data mining that are applicable to the Reward points repository.