Applications of Data Mining to Reward points collection service

Continuation of use cases: 

Collaborative filtering is another use case where the binary conditions apply. This is particularly useful when there are multiple participants in a group whose opinions determine the best grant of reward points. In the earlier approaches, the algorithms were articulating conditions. In this algorithm, we avoid the use of conditions and replace it with ratings. The participants in the group can be selected such that they form a diverse set or a cohesive set depending on the purpose. The calculation of grants based on existing reward points can be determined with the help of this opinion group and it helps to avoid many of the pitfalls with the logic associated with conditions. Some of these include the disclosure of rules, taking advantage of the rules, and circumventing them.

Hierarchical clustering is helpful when we want to cluster the reward points to match with the organizational hierarchy to give credit to the manager when their reporting employees do well. This is a standard practice in many companies. It may not be evident from the flat independent grants assigned to individuals that the reward points can be grouped based on the hierarchy to which the user belongs. Distance between members based on organizational hierarchy can also be used as a metric to determine the hierarchical clustering of reward point grants.

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.

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