圖型為具結構性的資料結構，能夠完整描述真實世界的資料關聯性。探勘常見子圖必須考慮子圖間為同質異構體的問題，因此處理複雜度高，應用在資料流探勘的成本更高。本論文考慮從單位時間的累積圖型資料集中探勘常見子圖樣式的問題，我們認為在資料流中資料樣式的改變會經過一個過渡期，因此未必在每一個時間點皆需要進行重新探勘。本論文將初始時間點探勘出的常見子圖樣式稱為基底常見子圖樣式，提出從新進累積圖型資料集中有效率地偵測比對是否包含基底常見子圖樣式的方法，近似估算出基底常見子圖樣式在新進累積圖型資料集中仍為常見的比例值，以判定資料流中常見子圖樣式趨勢轉移的情況，並建議需重新進行新資料中子圖樣式探勘的時間點。本論文所提出的FGCD方法建立不同的圖型資料結構，並運用常見子圖樣式的向下包含特性來加速比對過程。由實驗結果顯示，此方法可有效比對估算出新進累積圖型資料集中仍維持常見之基底子圖樣式，當樣式趨勢尚未發生改變時，比重新進行探勘在更快的時間內提供常見子圖樣式的近似結果。

Graph is a kind of structural data, which is applied to model the various relations among data in real world. Mining frequent sub-graph patterns, being equal to solve the problem of checking graph isomorphism, is a NP hard problem. Therefore, mining frequent sub-graph patterns in data streams is an even more complicated problem. In this thesis, graph data at every time point is collected for mining frequent sub-graph patterns at the time point. We assume that the changing of frequent sub-graph patterns will take several time points. Therefore, it is not necessary to re-mine frequent sub-graph patterns at each time point. The frequent sub-graph patterns discovered at the first time point are named base patterns. An efficient method, named FGCD algorithm, is proposed to detect the change of base patterns at the following time points, the FGCD algorithm approximately counts the frequencies of base patterns in the set of newly coming graphs, and calculates the percentage of remaining frequent patterns to decide whether the trend of frequent sub-graph patterns is changing or not and trigger to perform the re-mining of frequent sub-graph patterns. The storage structures of graphs are designed and the downward closure property among frequent sub-graphs is applied in the proposed method to efficiently match the sub-graphs patterns. According to experimental results, FGCD can approximately estimate the percentage of base patterns that remain frequent. When the trend of frequent sub-graph patterns does not change, FGCD algorithm provides a more efficient way than re-mining to maintain the frequent sub-graph patterns approximately.

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