一种基于密度的文本聚类挖掘算法

Text cluster mining algorithm based on density

针对DBSCAN算法需用户设置参数值、易产生挖掘结果偏差等不足,提出改进算法DBTC(density-basedtext clustering),该算法不仅能够发现任意形状的簇,还有效地解决了基于密度的DBSCAN聚类算法在文本挖掘中参数设置困难和高密度的簇被相连的低密度簇包含的问题。理论分析和实验结果表明,算法是有效可行的。

Focusing on the problem that the DBSCAN algorithm needs to set parameters by users and lends to warp the mining result, proposed an improved text clustering algorithms DBTC （density-based text clustering）. The algorithm not only could find arbitrary shaped clusters, but also efficiently solved these problems which were it was too difficult for users to determine the parameters and the high-density cluster was completely contained to the linked low-density cluster. Theoretic analysis and experimental results indicate that the algorithm is effective and efficient.