基於證據累加的叢集整合技術之強韌化與功能延伸

Abstract

叢集化是一個可以在沒有分類資訊的資料當中，將相關的資料點區分成叢集的方 法。叢集化演算法的種類很多，但並沒有一個方法可以對所有的資料與叢集性質都產 生好的結果。叢集整合(cluster ensemble) 技術是近年的一個新趨勢，其做法是對同一 組資料產生多個不同的叢集化結果，再結合這些個別結果來產生一個具有共識的、更 穩定也更能代表實際資料分佈的分群。叢集整合的優點最近已逐漸被證實，也有愈來 愈多的應用出現在不同的領域。 這個二年期計畫的整體目標是以證據累加叢集法(evidence-accumulation clustering) --也就是基於co-association 矩陣的叢集整合方法--為基礎，研討改善其強韌性與延伸 其應用範圍的方法。一方面，我們將證據累加叢集法與強韌叢集法做結合來改進其應 用到雜訊環境與未知叢集數量的問題時的效能。另一分面，我們也將叢集整合應用到 有特定叢集形狀的問題，以利用叢集整合的優點於使用叢集演算法偵測線段叢集、面 叢集、以及主曲線的演算法，以改善其結果。我們預期這些研究成果將對發展叢集整 合的應用有明顯的貢獻。

Clustering is a process that groups unlabeled data points into clusters. There are a large variety of clustering methods, but none can generate good clustering results for all types of data and cluster characteristics. Cluster ensemble is a new trend in recent years. Its approach is to generate multiple clustering results out of the same data set, and then combine the individual clustering results to form a consensus partition of the data that is more stable and more representative of the actual data distribution. As the benefits of cluster ensemble are gradually recognized in recent years, there are a growing number of applications in various fields. The overall purpose of this two-year project is to start with evidence-accumulation clustering, that is, the clustering ensemble methods based on co-association matrices, and investigate methods that can improve its robustness and extend its applications. First, we combine evidence-accumulation clustering with robust clustering algorithms to improve its performance in problems that involve noisy data and unknown numbers of clusters. Second, we apply cluster ensemble to clustering problems that identify clusters of particular shapes, so that the benefits of cluster ensemble can improve the results of detecting line-segment clusters, shell clusters, and principal curves. We expect the outcome of this project to have contribute substantially to developing applications of cluster ensembles.