鑒于目前傳統(tǒng)文本聚類方法中利用文檔間的相似度進(jìn)行聚類存在的問題，在傳統(tǒng)的文本挖掘基礎(chǔ)上提出了一種新的文本聚類算法—— 利用單詞超團(tuán)的二分圖文本聚類算法。該算法用文檔中單詞的關(guān)聯(lián)模式來評估文檔間的相似度及主題類別預(yù)測，并利用圖劃分策略來大大降低文檔相似度比較算法的復(fù)雜度，同時將超團(tuán)作為特征結(jié)構(gòu)的擴(kuò)展，可以在一定范圍內(nèi)減少語言信息的丟失，提高聚類效果。經(jīng)實驗證明該算法具有較高的有效性。
關(guān) 鍵 詞 二分圖劃分; 文本聚類; 單詞超團(tuán)

Abstract This paper proposes a new algorithm for document-word co-clustering. After mining semantics with word hyperclique patterns, the document dataset with a bipartite graph is described. Then, the efficient graph partitioning algorithm is employed to partition this graph, so that the high computational overhead of traditional clustering algorithms over huge document datasests can be avoided. During clustering, word hyperclique patterns that are full of document semantics are preserved. In this way, our algorithm partially circumvents the problem of loosing document semantics, which happens a lot in traditional clustering algorithms based on document pairwise similarity alone. Finally, the extensive experimental results demonstrated the effectiveness of this algorithm in document clustering accuracy and cluster topic detection.
Key words bipartite partition; documents clustering; word hyperclique