有效率的複合式後項關聯式法則探勘演算法-以壽險業為例

An Efficient Algorithm for Association Rule with Disjunctive Consequent–The Case of the Insurance Industry

陳智揚(Zhi-Yang Chen)

指導教授 ： 陳伯榮

淡江大學/工學院/資訊工程學系碩士班/碩士(2006年)

https://doi.org/10.6846/TKU.2006.00873

摘要

在資料採礦中，關聯式法則是經常被使用的技術之一，然而對於新上市的產品而言，關聯式法則的運用卻受到支持度及信賴區間最小門檻值的限制。

一般而言，只有當關聯法則A→B和A→C這兩條的支持度和信賴度皆高於最小門檻值時，才表示這兩條法則是有用的。但在現實生活中支持度低可能表示A為較晚推出的產品。另外，當A→B與A→C的信賴度未達門檻值時，並不表示說A→B∨C的信賴度也不會達到門檻值。

因此，本論文針對此種狀況，提出複合式後項關聯式法則探勘演算法，發掘出這類有用的規則。並將此法則運用於保險業的產品組合及行銷。由實證結果顯示，主要險種搭配特定的附險銷售時，消費者除了主險外也會一併購買附險。

關鍵字

關聯式法則 ； 複合式物品項目 ； 資料探勘 ； 壽險

並列摘要

The association rule is one of the frequently adopted techniques in data mining. However, it practically limited to the minimum support and confidence for newly marketed products.

When association rules A→B and A→C can not be discovered from the database, it does not mean that A→B∨C will not be an association rule from the same database. In fact, when A is the newly marketed product, A→B∨C shall be a very useful rule in some cases.

Therefore, we propose a new and very simple algorithm to discover this type of rules. Since the consequent item of this kind of rule is formed by a disjunctive composite item, we call this type of rules as the disjunctive consequent association rules. Moreover, when we apply our algorithm to insurance policy for cross selling, the useful results have been proven by the insurance company.

並列關鍵字

Association Rule ； Disjunctive Consequent ； Data Mining ； Insurance

參考文獻

[3].S. Brin, R. Motwani, J. Ullman, and S. Tsur, “Dynamic Itemset Counting and Implication Rule for Market Basket Data,” Proceedings of the 1997 SIGMOD Conference on Management of Data, pp. 255-264, 1997.

[5].J. Hipp, U. Güntzer, and G. Nakhaeizadeh, “Algorithms for Association Rule Mining – A General Survey and Comparison,” SIGKDD Explorations, Vol. 2, Issue 1, pp. 58-64, 2000.

[6].J. S. Park, M. S. Chen, and P. S. Yu, “Using a Hash-Based Method with Transaction Trimming for Mining Association Rules,” IEEE Transactions on Knowledge and Data Engineering, Vol. 9, No. 5, pp. 813-825, 1997.167.

[10].C. Bettini, X. S. Wang, and S. Jajodia, “Mining Temporal Relationships with Multiple Granularities in Time Sequences,” Data Engineering Bulletin, Vol. 21, pp. 32-38, 1998.

[13].P. Rolland, “FlExPat: Flexible Extraction of Sequential Patterns,” Proceedings of the IEEE International Conference on Data Mining 2001, pp. 481-488, 2001.