Abstract: Nowadays, there are three major challenges for short text clustering, the sparsity of feature key, the complexity of processing in high-dimensional space and the comprehensibility of clusters. For these challenges, a K-means clustering algorithm is proposed, which is improved by combining with semantic. Short text is described by collection of words in this algorithm, it alleviates the sparsity problem of characteristics of short text keywords. The clustering center can be obtained by mining the maximum frequent word set of short text collection, which effectively overcomes the defect that K-means clustering algorithm is sensitive to the initial clustering center, it solves the problem of the comprehensibility of clusters, and avoids the operation in high-dimensional space. The experimental results show that short text clustering algorithm combined with semantic is better than traditional algorithms.

Key words: text mining, clustering of short text, K-means algorithm, maximum frequent word set, HowNet, semantic similarity

摘要： 针对短文本聚类存在的三个主要挑战，特征关键词的稀疏性、高维空间处理的复杂性和簇的可理解性，提出了一种结合语义改进的K-means短文本聚类算法。该算法通过词语集合表示短文本，缓解了短文本特征关键词的稀疏性问题；通过挖掘短文本集的最大频繁词集获取初始聚类中心，有效克服了K-means聚类算法对初始聚类中心敏感的缺点，解决了簇的理解性问题;通过结合TF-IDF值的语义相似度计算文档之间的相似度，避免了高维空间的运算。实验结果表明，从语义角度出发实现的短文本聚类算法优于传统的短文本聚类算法。

关键词: 文本挖掘, 短文本聚类, K-means算法, 最大频繁词集, 知网, 语义相似度