Multidimensional Data Mining for Healthcare Service Portfolio Management

Data Mining is one of the most significant tools for discovering association patterns that are useful in for health services, Customer Relationship Management (CRM) etc.. Yet, there are some drawbacks in conventional mining techniques. Since most of them perform the plain mining based on predefined schemata through the data warehouse as a whole, a re-scan must be done whenever new attributes are added. Secondly, an association rule may be true on a certain granularity but fail on a smaller one and vise verse. Last but not least, they are usually designed specifically to find either frequent or infrequent rules. With regard to healthcare service management, this research aims at providing a novel data schema and an algorithm to solve the aforementioned problems. A forest of concept taxonomies is used as the data structure for representing healthcare associations patterns that consist of concepts picked up from various taxonomies. Then, the mining process is formulated as a combination of finding the large itemsets, generating, updating and output the association patterns. Crucial mechanisms in each step will be clarified. At last, this paper presents experimental results regarding efficiency, scalability, information loss, etc. of the proposed approach to prove the advents of the approach.