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題名：成長式自組織映射圖視覺化多維度混合型資料
作者：戴偉勝 引用關係
作者(外文)：Wei-Shen Tai
校院名稱：國立雲林科技大學
系所名稱：資訊管理系博士班
指導教授：許中川
學位類別：博士
出版日期：2012
主題關鍵詞：自組織映射圖；資料探勘；混合型資料；資料視覺化；data mining；Self-Organizing Map (SOM)；mixed-type data；data visualization
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現今許多實務應用中，隨處可見大量包含數值型與類別型屬性的多維度混合型資料。因此，如何有效地處理進而分析這些混合型資料，儼然成為資料探勘領域的一項重要課題。一般而言，藉由視覺化模式的協助，複雜資料間的關係較容易被理解與分析。這使得具有將高維度資料特性視覺化呈現於低維度圖形能力的自組織映射圖，成為擷取大量資料之中重要資訊的一樣利器。
近年來，許多自組織映射圖的延伸模式不斷地被提出，冀期改善傳統自組織映射圖中固定圖形大小、拓樸維持與處理混合型資料的缺失。例如：成長式自組織映射圖採用彈性圖形結構概念以克服固定圖形大小的限制，以及應用將資料間圖形距離納入考量的修正函式，以增進預設圖形投射結果的拓樸維持程度。然而，目前為止沒有自組織映射圖模式提供一個可以同時解決上述問題的具體方案。為此，本研究提出一個成長式混合型自組織映射圖，藉由整合新提出的動態結構機制、視覺歸納修正與距離階層等方法以解決上述問題於單一模式中。實驗結果證實，本研究所提出的模式確能為上述問題提供一個新的解決之道，以妥善處理混合型資料以及於彈性結構圖形中呈現出資料的空間距離。
Nowadays, abundant multivariate mixed-type data including numeric as well as categorical attributes are ubiquitous in a variety of applications. Therefore, processing and analyzing such mixed-type data has become an important issue in data mining field. Via visualization models, one is able to understand and analyze those relationships between complicated data more effortlessly. Self-Organizing Map (SOM) possesses an effective visualization capability for presenting the characteristics of high-dimensional data on a low-dimensional map. One can efficiently extract valuable information from a large amount of data by means of SOM mapping results.
More recently, multitudinous variants of SOM were devised to improve deficiencies occurred in conventional SOMs such as fixed-size map, topological preservation and mixed-type data. To overcome the constraint of fixed-size map, diverse flexible map structures were proposed in many growing SOMs. On the other hand, a varied update function in which the distances of map-space between data were considered was used to enhance topological preservation of projected resultants on a predetermined map. Nevertheless, none of current models offers a plausible solution to solve the foregoing problems simultaneously. In this study, a Growing Mixed-type SOM (GMixSOM) is proposed to overcome the abovementioned deficiencies by integrating a new dynamic structure scheme, visualization-induced update and distance hierarchy in one model. Experimental results demonstrated that the proposed model is a feasible solution to manipulate multivariate mixed-type data and reflect the data-space distance between data on a map with a flexible structure.
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