在圖學之中，挖掘極大完全二分圖(Maximal Bicliques)在二分圖(Bipartite)探勘中是很重要的一環，極大完全二分圖的應用不僅限於蛋白質分析，同時可以分析搜尋引擎的關鍵字搜尋，以及賣場的交易資料關聯性分析(封閉項目集)等等，對於企業或管理者可以獲得更多的金錢利益。
然而，某些演算法因為需要濾除重複的極大完全二分圖需要使用記憶體來儲存，導致在極大完全二分圖很多時會有記憶體不足的問題，或者有的演算法因為需要做支持度(Support)的運算而去掃描整個數據庫，造成不必要的時間浪費等，這些問題需要我們藉由研發新的演算法來一一克服。
因此，我們提出了MBC_SP演算法，就是以切割子超立方體搜尋空間的概念做深度優先搜尋(Depth-First-Search)來完成所有的極大完全二分圖探勘，並且藉由減少搜尋空間的方式來減少演算法執行時間。
MBC_SP是藉由子超立方體的分解來切割成獨立的搜尋空間來搜尋出所有的極大完全二分圖，此演算法不僅適合做平行處理來加快演算法執行效率，同時也可以減少不必要的搜尋空間以及解決重複產生極大完全二分圖的問題，對於效能的加速是一大優勢。

In graphics, mining maximal bicliques is a very important part in the bipartite graph exploration. Mining maximal bicliques can be employed in protein analysis, key analysis of search engines, and correlation analysis of closed itemsets in store transaction data, etc. It can obtain more monetary benefits for enterprises or managers.
However, some algorithms need to use memory to store the found maximal bicliques that need to filter out duplicates, resulting in insufficient memory when there are extremely large maximal bicliques. Some algorithms need to scan the entire database for support calculations, resulting in wasting lot of calculation time on scanning the database. These problems need to be overcome one by one by developing new algorithms.
Therefore, we proposed the MBC_SP algorithm, which uses the concept of cutting the subcube search space to facilitate maximal bicliques exploration, and then reducing the search space, resulting in reducing the execution time significantly.
MBC_SP divides the subcube into independent search spaces to search for all the maximal bicliques. This algorithm is not only suitable for parallel processing to speed up the execution efficiently, but also reduces unnecessary search space. It can filter out duplicates completely. The experiment results show that the proposed algorithm can achieve performance improvement significantly.

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