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Comparing frequent patterns: A study case of Apriori and singleton implementations in a diabetes type 2 data set

Frequent sequential pattern (FSP) mining has become an effective tool to explore the pattern sequence occurrences in many fields. The methods developed in FSP is mainly based on Apriori algorithm. This algorithm looks for frequent sequence of itemset which need not to be consecutive. In addition, the itemset that supports the cardinality of a frequent sequence can be a partial itemset. However, in the case of medication for diabetes type 2, the selection of patient medication is considered essential. A combination of medications represents the clinical conditions of the patients. Therefore, we considered a medication combination as one full item sets (i.e., singleton). We are interested in the transition events from one medication episode to the next. As such, we consider consecutive sequence of singleton. This paper studies the result characteristic of Apriori-based FSP and singleton mining. The result of this study shows that the singleton mining results set is the subset of Apriori-based algorithm, with 0.203 of ratio value. However, Apriori-based algorithm results set contains frequent sequence pattern of medication transition event which is unlikely to happen in real clinical conditions with high frequency. By contrast, the singleton mining results set represents the true medication transition event.