政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/130956

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/130956

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政大典藏 > College of Commerce > Department of Statistics > Theses > Item 140.119/130956

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/130956

Title:	有大小限制之切割式分群演算法 Partitional Clustering Algorithms with Size Constraints
Authors:	粘明揚 Nian, Ming-Yang
Contributors:	洪英超 Hung, Ying-Chao 粘明揚 Nian, Ming-Yang
Keywords:	非監督式學習法切割分群演算法有限制的分群演算法尺寸限制分群演算法梯度下降法位區途程問題
Date:	2020
Issue Date:	2020-08-03 17:31:26 (UTC+8)
Abstract:	分群演算法是常見且重要的非監督式學習法。在實際應用上，我們有時必須考量分群樣本個數的大小尺寸限制，這是一般傳統分群演算法做不到的。在本篇論文中，我們提出有大小限制的切割式分群演算法，其流程類似於Lloyd 的演算法，終止迭代直至中心點收斂，盡量最小化分群的目標函式，且每一群的樣本數都滿足預先設定的尺寸限制。而我們的演算法主要有兩個部分，第一個部分為調整群樣本個數至滿足限制條件，在面對不平衡資料時，分群結果往往優於傳統分群演算法。第二個部分則是梯度下降法，藉由此部分來求得最佳中心點，也能夠避免群中心點受到極端值影響的情況(如K-means 演算法)，進而改善分群結果。在電腦模擬與實證分析方面，本文將所提出的演算法除了可以處理不平衡資料分群，還能解決汽車服務系統的位區途程策略問題(Location-Routing Problem，LRP)。除此之外，本文也就目標函式值大小以及演算法所耗費的運算時間和文獻中其他方法做比較，電腦模擬的結果證明本文所提之演算法無論是準確度和速度皆遠高於文獻中所提之方法。
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Description:	碩士國立政治大學統計學系 107354014
Source URI:	http://thesis.lib.nccu.edu.tw/record/#G0107354014
Data Type:	thesis
DOI:	10.6814/NCCU202000924
Appears in Collections:	[Department of Statistics] Theses

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