臺灣博碩士論文加值系統

由於行銷環境的快速變化，傳統的行銷策略及技術均受到衝擊，現代的公司皆已捨去大量行銷及產品差異化行銷，轉向目標行銷。藉由目標行銷銷售者也可以依據消費者對產品不同的需求，將產品區分一個或多個市場區隔，可以有效的調整產品的價格、零配件規格和配銷通路等，以求更接近目標市場。分群技術在市場區隔領域中，常被用來透過群落的分析將資料依照不同樣式、特徵區隔成若干個群組，在依據各群組加以應用，以求得有效的分析及預測等結果。故本研究提出一個限制式分群基因演算法，透過分群技術將這些龐大數目的消費記錄分組，以減低分析及應用上的複雜度，也藉由產品關聯搜尋，了解不同產品之間的關係，讓行銷人員在規劃促銷或是行銷活動時，可以針對消費者需求，推薦最適當的產品給消費者，以增加顧客滿意度，更有效的提高公司的利潤。最後，建立雛型系統並藉由實驗來驗證其效益，而研究結果證明本研究提出的方法明顯的優於其他方法，也證實限制式的應用確實可以使分群達到更好的品質。

As the rapidly change of the marketing environment, traditional marketing strategy and the technology are under the affect, many company all have given up the Mass marketing and Differentiation, and then change to the Target marketing. The target marketing can base on customers’ different needs to segment one or more market. It can be effectively to adjust the price of product, the specifications of spare parts and distributions. Clustering technology in the segmentation marketing is often used to analyze the data according to different styles and through the features to separate into several groups, applying the group to achieve effective analysis and prediction the results. Therefore, this study has proposed a constrained genetic algorithm clustering, we used clustering technology to deal with the large number of consumer records and reduce the complexity of analysis. We also used associate rule at product to understand relationships in products. It can let marketing manager planning of promotional or marketing activities which the consumer demand is the most appropriated products and recommend to it. It’s also increased customer satisfaction and improved the company’s profits. Finally, we have built a prototype and verified its effectiveness by experiments. The experiments showed that our method is better than the others, and approved that the constraints can actually promote the clustering to get much better quality.

中文摘要 i
英文摘要 ii
誌 謝 iii
目 錄 iv
圖索引 v
表索引 vi
第一章 緒論 1
1.1研究背景與動機 1
1.2研究目的 5
1.3研究架構與流程 6
1.4研究限制與範圍 7
第二章 文獻探討 8
2.1市場區隔 8
2.2分群技術 12
2.3分群相關應用 17
第三章 研究方法 18
3.1基因演算法 18
3.2分群基因演算法 25
3.3約略集合理論 28
3.4 Apriori演算法-產品關聯搜尋 40
3.5 限制式分群基因演算法 45
第四章 雛型系統與實驗分析 50
4.1系統描述 50
4.2範例說明 51
4.3實驗與分析 66
第五章 結論 76
參考文獻 78

圖索引
圖 3-1 基因演算法流程圖 19
圖 3-2 單點交配(灰框為交配部份) 22
圖 3-3 兩點交配(灰框為交配部份) 22
圖 3-4 均勻交配(灰框為交配部份) 23
圖 3-5 單點突變(灰框為突變部份) 23
圖 3-6 上下界近似 31
圖 3-7 Apriori演算法流程圖 41
圖 3-8 限制式基因分群演算法 45
圖 4-1 消費者交易資料庫部分資料 50
圖 4-2 限制式分群基因演算法收斂圖 60
圖 4-3 產品總類整理 61
圖 4-4 消費者購買產品種類整理 62
圖 4-5 27組參數敏感度分析 74

表索引
表 2-1 市場區隔相關文獻整理 11
表 2-2 限制式分群相關應用 16
表 2-3 分群相關應用 17
表 3-1基因演算法相關應用 24
表 3-2分群基因演算法參數符號定義 25
表 3-3 原始資料表 29
表 3-4 不可區分關係(B={a1,a2,a3}) 31
表 3-5 移除的屬性對不可需分關係子集合的影響 33
表 3-6 識別矩陣 34
表 3-7 經識別矩陣處理後的資料 35
表 3-8 識別矩陣 36
表 3-9 經識別矩陣簡化後的資料 37
表 3-10約略集合相關應用 38
表 3-11 Apriori常用詞定義 42
表 3-12 Apriori演算法相關應用 44
表 4-1 屬性描述 52
表 4-2 交易資料庫資料集合 53
表 4-3交易資料庫基本集合 53
表 4-4 交易資料庫上下界近似值與準確率 54
表 4-5 識別矩陣 54
表 4-6屬性的折減結果 55
表 4-7識別矩陣 55
表 4-8屬性值折減 56
表 4-9相關的決策規則 56
表 4-10 初始母體 57
表 4-11 第5代母體 58
表 4-12 第15代母體 59
表 4-13 第17代母體 59
表 4-14 分群結果 60
表 4-15 消費者交易資料集合 62
表 4-16 各候選項目集之支持度 63
表 4-17 項目集的合併流程 63
表 4-18 2-項目集合 64
表 4-19 3-項目集 65
表 4-20 實驗1運算結果比較 68
表 4-21實驗2運算結果比較 69

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