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研究生： 王韋棟
Wei-tung Wang
論文名稱： 依據資料而修正之基於密度與雜訊辨別聚類分群法
Adaptive Density-based Spatial Clustering of Applications with Noise (DBSCAN) According to Data
指導教授： 吳怡樂
Yi-Leh Wu
口試委員: 陳建中
none
唐政元
none
閻立剛
none
學位類別： 碩士
Master
系所名稱： 電資學院 - 資訊工程系
Department of Computer Science and Information Engineering
論文出版年： 2014
畢業學年度： 102
語文別： 英文
論文頁數： 67
中文關鍵詞： 資料探勘 、DBSCAN 、分群演算法
外文關鍵詞： Data mining, Clustering, DBSCAN
相關次數： 點閱：167  下載：4 
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    • 分群是一依據資料某些特性而將其分成不同群聚的技術。DBSCAN為一個基於密度的分群演算法。DBSCAN演算法中需要兩個使用者自訂的參數，而這兩個參數在使用者未對資料有事先研究過的情況下往往難以決定，但卻對分群的結果有著顯著的影響。
    在密度有變化的資料中，DBSCAN也不容易分出正確的群。我們修改了原來的DBSCAN，主要的概念是讓DBSCAN在不同的密度資料有著不同的參數，使其能依據資料分佈而自行決定資料中各密度階層所對應的參數，也讓DBSCAN在密度變化大的資料中，可以改善分群的結果。

    Clustering is a task that aims to grouping data objects into several groups. DBSCAN is a density-based clustering method. However, it requires two parameters and these two parameters are hard to decide. Also, DBSCAN has difficulties in finding clusters when the density changes in the dataset. In this paper, we modify the original DBSCAN to make it able to determine the appropriate eps values according to data distribution and to cluster when the density varies among dataset.
    The main idea is to run DBSCAN with different eps and Minpts values. We also modified the calculation of the Minpts so that DBSCAN can have better clustering results. We did several experiments to evaluate the performance. The results suggest that our proposed DBSCAN can automatically decide the appropriate eps and Minpts values and can detect clusters with different density-levels.

    論文摘要1 ABSTRACT 2 CONTENTS 3 LIST OF FIGURES4 LIST OF TABLES7 CHAPTER 1. INTRODUCTION8 CHAPTER 2. DBSCAN10 CHAPTER 3. MODIFIED DBSCAN14 3.1 DETERMINING EPS14 3.2 EVALUATING MINPTS17 3.3 ADDITIONAL EPS AND MINPTS18 CHAPTER 4. EXPERIMENTS AND RESULTS21 4.1 DATASET AND SETUP21 4.2 PERFORMANCE AND EVALUATION21 4.3 COMPARISONS44 4.4 OPTIMAL K56 4.5 DISCUSSION OF EXPERIMENTS60 CHAPTER 5. CONCLUSIONS AND FUTURE WORK61 REFERENCES62

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    [11] Clustering datasets, http://cs.joensuu.fi/sipu/datasets/, referenced on April 1st, 2014.
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    [13] UC Irvine Machine Learning Repository, https://archive.ics.uci.edu/ml/index.html, referenced on March 1st, 2013.
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    [16] Accuracy and precision, https://en.wikipedia.org/wiki/Accuracy_and_precision, referenced on May 1st, 2014.
    [17] C. T. Zahn, “Graph-Theoretical Methods for Detecting and Describing Gestalt Clusters”, IEEE Trans. Comput. 20, 1 (January 1971), 68-86. DOI=10.1109/T-C.1971.223083 http://dx.doi.org/10.1109/T-C.1971.223083, 1971.
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