整合集群分析與螞蟻理論於關聯法則之探勘

Integration of Cluster Analysis and Ant Colony System in Association Rule Mining

林思宇(Szu-Yu lin)

指導教授 ： 郭人介

國立臺北科技大學/管理學院/工業工程與管理系所/碩士(2005年)

摘要

在知識經濟的時代，除了分享、應用社群中的知識外，知識的發現也成為了一個很重要的議題，而資料探勘在知識發現的過程裡扮演了一個很重要的角色，在此本研究嘗試一個新的資料探勘架構：先叢集分析，再進行關聯式法則的探勘；透過此一架構，期望能以最小的誤差下，達成提升資料探勘效能，使得資料探勘更容易應用於真實世界上。
本研究首先將醫療資料庫中的疾病代碼透過國際疾病分類碼（ICD-9-CM）的分類縮減資料的維度，再透過自組織映射圖（SOM）進行集群分析，最後再利用以螞蟻理論為基礎的關聯法則探勘方式，對各集群進行關聯法則的探勘，發現透過此一資料探勘架構，不但可以提升探勘的效率，透過集群的分析，可以輕易的探索某特定一部分的關聯法則，以利在面對龐大資料下，可以更輕易的掌握到有用的知識。

關鍵字

資料探勘 ； 螞蟻集群系統 ； 集群分析 ； 自組織映射圖 ； 關聯法則

並列摘要

In addition to sharing and applying the knowledge in the community, knowledge discovery has become an important issue in the knowledge economic era. Data mining plays an important role of knowledge discovery. Therefore, this study intends to propose a new framework of data mining that does clustering analysis first, and then followed by association rule mining.
The study reduced the data dimensions by the classifications of International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) first, and then clustered the data set with Self-organizing Map(SOM) network. Finally, we mined the association rule in all clusters by ACS-based association rule mining system. The result showed that the new mining framework can provide not only the better effect, but also the easier way to find the useful rules that maybe hidden in the very large data. In other words, it is easier to extract the useful knowledge by the proposed framework.

並列關鍵字

Data mining ； Ant colony system ； Association rule ； Cluster ； SOM

參考文獻

[9] A. K. Jain, M. N. Murty, P. J. Flynn, “Data Clustering: A Review,” ACM Computing Surveys, Vol. 31, No. 3, September, 1999.

[11] B. D. Amir, S. Ron, Y. Zohar, “Clustering Gene Expression Patterns,” Journal of Computational Biology, Vol.6, pp.281-297, 1999.

[12] C. F. Tsai, H. C. Wu, and C. W. Tsai, “A New Clustering Approach for Data Mining in Large Databases,” Proceedings of the international Symposium on Parallel Architectures, Algorithms and Networks (ISPAN’02), IEEE Computer Society, pp.1087-4089, 2002.

[14] H. Maulik, and S. Bandyopadhyay, “Genetic Algorithm-Based Clustering Technique,” Pattern Recognition, Vol.33, pp. 1455-1465, 2000.

[15] H. Ressom ,D. Wang, P. Natarajan, “Adaptive double self-organizing maps for clustering gene expression profiles,” Neural Networks Vol. 16 , Issue 5-6 pp.633 - 640, June 2003.