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題名 | 高效率之遞增式探勘演算法--QPD=An Efficient Incremental Mining Algorithm--QPD |
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作者 | 黃仁鵬; 黃南傑; 郭煌政; | 書刊名 | 商管科技季刊 |
卷期 | 7:1 民95.03 |
頁次 | 頁27-57 |
分類號 | 312.13 |
關鍵詞 | 資料探勘; 關聯規則; Apriori演算法; 高頻項目集; 遞增式探勘; Data mining; Association rule; Apriori; Frequent itemsets; Incremental mining; |
語文 | 中文(Chinese) |
中文摘要 | 近年來,客戶關係管理(CRM)是個相當熱門的議題,因為企業必須了解消費者購物行為與商品間的關聯關係,才能妥善安排商品陳列順序。如此可以提昇客戶滿意度,減少購物的搜尋時間。再者可以刺激購買商品數量,用以增加企業的利潤。所以在大型交易資料庫中,利用資料探勘技術找出有用的關聯規則,來提供企業的決策支援是非常重要的。 本研究提出新的演算法QPD(Quick Patterns Decomposition)來找出商品問的關聯規則。QPD 演算法的優點如下:l.只需掃描資料庫一次;2.利用型樣化方式來提昇執行效率; 3.利用遮罩(mask)與布林模式(Boolean)來產生拆解項目因子型樣;4.當最小門檻值變動時,不需重新探勘;5.資料庫有異動時,可方便進行漸進式探勘。 上述得知,透過本演算法做關聯分析,其效能將優於以往Apriori -Base的演算法。 此外,關聯規則的推導過程中,將不會重複產生多餘的候選項目組,因此更勝於拆解模式的演算法。快速得到正確、有效用的資訊,是企業在數位時代中最大的利器,由此能降低時間成本、快速反映市場需求,是提昇競爭力的最大利基。 |
英文摘要 | Recently Customer Relationship Management is one of the hottest issues in cooperation. In order to properly arrange the positions of products, Cooperation need to understand customers' shopping behaviors and the associations between products. In this way, we can increase the customers' satisfactions and decrease the searching time during shopping. Besides, we can increase the quantity of purchase products and the profits. Thus, it is very important to use the technology of data mining to find the useful association rules and to provide the cooperation's decision supports. In this paper we propose a new algorithm QPD (Quick Patterns Decomposition) to find the association rules from large transaction databases. The merits of QPD algorithm are: 1. In data mining process it only needs to scan whole transaction database once. 2. Using Patterns method to increase the performance of data mining process. 3. Using mask and Boolean method to decompose the itemsets to sub-itemsets. 4. When minimum support changed we do not need to process mining process again. 5. It does not need to rescan the original database for mining the association rules from the incrementally growing databases. From above illustration we know that by using QPD algorithm to process association analysis has better performance than Apriori-based algorithms. In association rule's reasoning process, it won't produce the unnecessary candidate items. Therefore it can fast obtain the information correctly and effectively and reduce the time cost, and fastly reflect the market demand. That will greatly promote the competitive advantage. |
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