由於資訊科技的快速成長，企業需要存儲和處理的資料量變得越來越大。因此，企業需如何有效地運用這些資料是非常重要的問題。基本上，資料探勘技術可以幫助我們分析大型的資料庫，並從中提取了一些有趣或隱藏的資訊。尤其是分群分析，它能夠將資料進行分組到一些群集中。

因此，本研究試圖提出兩個分群方法，整合人工免疫網絡(aiNet)及K-means(aiNetK)和整合aiNetK及粒子群最佳化演算法(PSO) (aiNet-PSKO)。aiNetK方法主要是嘗試整合ai-Net與 K- means演算法，而aiNet - PSKO打算整合aiNetK與粒子群最佳化演算法。為了驗證所提出的方法，首先採用四個標竿資料集，Iris、Glass、Wine及Breast Cancer。接著，我們將提出的方法與PSO、PSKO、AIS、AISK和ai-Net演算法做效能比較，計算結果顯示aiNet - PSKO優於其他方法。之後，這些方法會被應用到網路花店的交易資料庫中。研究結果也顯示了aiNet – PSKO方法具有最低的SED值。我們可以針對不同的群集進一步施行行銷策略。然後，客戶可以得到他們想要的不同產品或是服務。

Due to fast growth of information technology, the quantity of data which are stored and processed by the enterprises has become larger and larger. Therefore, how to effectively apply these data is a very important issue for the enterprises. Basically, data mining techniques can help us analyze the large database and extract some interesting or hidden patterns. Especially the cluster analysis, it is able to group the data into some clusters.
Thus, this study attempts to propose two clustering methods, artificial immune network (AINET) K-means (AINETK) and hybrid of AINETK, particle swarm optimization (PSO) and K-means (AINET-PSKO). AINETK method mainly tries to integrate AINET with K-means algorithm, while AINET-PSKO intends to AINETK with particle swarm optimization. In order to verify the proposed methods, four benchmark data sets, Iris, Glass, Wine, and Breast Cancer, are first employed. The performances of the proposed methods are compared with those of PSO, PSKO, AIS, AISK, and AINET algorithms. The computational results indicate that AINET-PSKO outperforms other methods. Thereafter, these methods are applied to the transaction database for an internet florist. The results also show that the AINET-PSKO method has the lowest SED value. We can further use them to make the marketing strategies for different clusters. Then, the customers can get different products or services they prefer.

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