Multi-dimensional Multi-granularities Data Mining for Discovering Innovative Healthcare Services


  • Johannes K Chiang National Chengchi University
  • Chia-Chi Chu Department of Management Information Systems, Cloud Computing and Operation Innovation Center



Multidimensional Data Mining, Healthcare Services, Customer relationship Management (CRM), Association Pattern, Granular Computing.


Data Mining is getting increasingly important for discovering association patterns for health service innovation and Customer Relationship Management (CRM) etc. Yet, there are deficits of existing data mining techniques. Since most of them perform a 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 to find either frequent or infrequent rules.

After a survey of a category of significant health services, we propose a data mining algorithm alone with a forest data structure to solve aforementioned weaknesses at the same time. At first, we construct a forest structure of concept taxonomies that can be used for representing the knowledge space. On top of it, the data mining is developed as a compound process to find the large-itemsets, to generate, to update and to output association rules that can represent services portfolio. After a set of benchmarks derived to measure the performance of data mining algorithms, we present the performance with respect to efficiency, scalability, information loss, etc. The results show that the proposed approach is better than existing methods with regard to the level of efficiency and effectiveness.

Author Biographies

Johannes K Chiang, National Chengchi University

Department of Management Information Systems,Cloud Computing and Operation Innovation Center

Chia-Chi Chu, Department of Management Information Systems, Cloud Computing and Operation Innovation Center

National Chengchi University



R. Agrawal and J. C. Shafer (1996). “Parallel Mining of Association Rules,” IEEE Transactions on Knowledge and Data Engineering, vol. 8, no. 6, pp. 962-969.

R. Agrawal and R. Srikant (1994). “Fast Algorithms for Mining Association Rules in Large Databases,” in Proceedings of the 20th International Conference on Very Large Data Bases.

R. Agrawal, T. Imielinski and A. N. Swami (1993). “Mining Association Rules between Sets of Items in Large Databases,” in Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data.

J. K. Chiang (2007). “Developing an Approach for Multidimensional Data Mining on various Granularities ~ on Example of Financial Portfolio Discovery,” in ISIS 2007 Proceedings of the 8th Symposium on Advanced Intelligent Systems, Sokcho City, Korea.

J. K. Chiang and J. C. Wu (2005). “Mining Multi-Dimension Rules in Multiple Database Segmentation-on Examples of Cross Selling,” in Proceedings of the 16th International Conference on Information Management, Taipei, Taiwan.

T. M. Cover and J. A. Thomas (2006). Elements of Information Theory, 2nd ed., Wiley.

R. Feldman and J. Sanger (2007). The Text Mining Handbook – Advanced Approaches in Analyzing Unstructured Data, Cambridge University Press.

J. Han and M. Kamber (2006). Data Mining - Concepts and Techniques, 2nd ed., Morgan Kaufman.

L. J. He, L. C. Chen and S. Y. Liu (2003) “Improvement of AprioriTid Algorithm for Mining Association Rules,” Journal of Yantai University(Natural Science and Engineering Edition), vol. 16, no. 4.

B. Lent, A. Swami and J. Widom (1997). “Clustering Association Rules,” in Proceedings of the 13th International Conference on Data Engineering.

M. Li and M. Baker (2005). The GRID – Core Technologies, Wiley.

B. Liu, W. Hsu and Y. Ma (1999), “Mining Association Rules with Multiple Minimum Supports,” in Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.

G. Shmueli, N. R. Patel and P. C. Bruce (2007).“Association Rules,” in Data Mining for Business Intelligence, Concepts, Techniques, and Applications, Wiley, pp. 203-215.

R. Srikant and R. Agrawal (1995). “Mining Generalized Association Rules,” in Proceedings of the 21th International Conference on Very Large Data Bases, Zurich, Switzerland.

W. Stallings (2004). “Channel Capacity,” in Business Data Communications, 6th ed., Pretice Hall, pp. 470-471.

P. S. Tsai and C. M. Chen (2004). “Mining interesting association rules from customer databases and transaction databases,” Information Systems, vol. 29, no. 8, p. 685–696.

C. Vercellis (2009). “Association Rules,” in Business Intelligence, Data Mining and optimization for Decision Making, Wiley, pp. 277-290.

The CRISP-DM Consortium, CRISP-DM 1.0 (2000),




How to Cite

Chiang, J. K., & Chu, C.-C. (2014). Multi-dimensional Multi-granularities Data Mining for Discovering Innovative Healthcare Services. British Journal of Healthcare and Medical Research, 1(3), 12–30.