在本論文中，我們設計一個利用傳統的循序樣式探勘技術，從社群網路串流資料中，能有效率地探勘出具有時間演進代表性循序樣式。此外，提出一個新的演算法Streaming Evolution Pattern Miner (SEPM)，有效的提升探勘效率與維護頻繁序列。SEPM還採用了一個修剪策略，有效地減少記憶體使用，最後使用虛構資料集的實驗結果表示SEPM於社群網路串流中有高效地執行效率。 In recent years, due to the growth of social website, many studies have focused on discovery useful knowledge from the social networks. Currently, nearly all methods are based heavily on graph theory method. To find some frequent or representative subgraphs from the large social data. However, the graph-based approach is very inefficient identifying frequent subgraphs. Even if some methods of accelerating encoding or pruning strategies are implemented, performance is not improved significantly.
Today''s social networking community information is very large. Based on graph theory the sub-graph exploration methods are usually only applicable to small and medium-sized graphics data sets. They cannot handle large-scale social network graph data. Community information continually changes over time. There will be new nodes or new edges joining the set. Similarly, old nodes or edges will need to removed. This method can only be used to represent a snapshot of the static social network of streaming data, and cannot represent the evolution of the changes brought about by these are based on the graph theory methods criticized area.
In this paper, we devised a method of using traditional sequential pattern mining technology from the social network streaming data to effectively discovery the evolution of representative sequential pattern over time. In addition, a new algorithm, Streaming Evolution Pattern Miner (SEPM), is proposed to effectively improve the efficiency of exploration and to maintain frequent sequences. Additionally, SEPM uses a pruning strategy to effectively reduce the use of memory. Finally, we utilize the synthetic dataset of experimental results to display improved SEPM social network streaming efficiency.
