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| 題名: | 基於 Plaid 演算法的雙向分群缺失值插補方法
A Biclustering Approach to Missing-Value Imputation Based on the PLAID Algorithm |
| 作者: | 林詠盛
Lin, Yung-Sheng |
| 貢獻者: | 吳漢銘
Wu, Han-Ming
林詠盛
Lin, Yung-Sheng |
| 關鍵詞: | 缺失值補值
雙向分群
PLAID 演算法
Missing data imputation
Biclustering
PLAID algorithm |
| 日期: | 2025 |
| 上傳時間: | 2025-08-04 15:12:22 (UTC+8) |
| 摘要: | 在資料分析過程中，缺失值的處理是極為關鍵的一步，尤其是在生物資訊領域中，資料集常常包含缺漏的數值，這可能會削弱研究結果的有效性。目前常用的補值方法如多重插補（Multiple Imputation）與最近鄰插補法（K-Nearest Neighbors, KNN），皆存在明顯的限制。多重插補仰賴強烈且往往難以驗證的隨機假設，而 KNN 在高維資料中則表現不佳。為了解決這些問題，我們提出一種基於 PLAID 雙向分群（biclustering）演算法的新型補值框架。PLAID 能夠偵測資料中的重疊模式與區塊結構，有效捕捉在基因表現與臨床資料中常見的局部共變異與功能模組。透過這些結構導引補值，我們的方法能實現具有生物學意義且具情境關聯性的缺值處理。我們進行模擬實驗與實際資料分析，並與現有方法進行比較，結果顯示，相較於傳統方法，善用雙向叢集結構能帶來更準確且更具生物學意涵的補值結果。
Missing value imputation is a critical step in data analysis, especially in bioinformatics, where datasets frequently contain missing entries that can undermine the validity of results. Current imputation methods, such as multiple imputation and k-nearest neighbors (KNN), have notable limitations. Multiple imputation depends on strong, and often untestable, stochastic assumptions, while KNN suffers from poor performance in high-dimensional data. To address these challenges, we propose a new imputation framework based on the PLAID biclustering algorithm. PLAID detects overlapping patterns and block structures in the data, capturing localized co-variation and functional modules commonly found in gene expression and clinical datasets. By using these structures to guide imputation, our method ensures biologically coherent and context-aware missing data handling. Through simulation studies and real-world data analyses, we compare our approach with existing methods. The results demonstrate that leveraging biclustering structures leads to more accurate and biologically meaningful imputation compared to conventional techniques. |
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| 描述: | 碩士
國立政治大學
統計學系
112354029 |
| 資料來源: | http://thesis.lib.nccu.edu.tw/record/#G0112354029 |
| 資料類型: | thesis |
| 顯示於類別: | [統計學系] 學位論文
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