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A Novel Short Text Clustering Model Based on Grey System Theory

机译：基于灰色系统理论的新型短文本聚类模型

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Short text clustering has great challenges due to the structural reasons, especially when applied to small datasets. Limitednumber of words leads to a poor-quality feature vector, low clustering accuracy, and failure of analysis. Although someapproaches have been observed in the related literature, there is still no agreement on an efficient solution. On the otherhand, the Grey system theory, which gives better results in numerical analyses with insufficient data, has not yet been appliedto short text clustering. The purpose of our study is to develop a short text clustering model based on Grey system theoryapplicable to small datasets. In order to measure the efficiency of our method, book reviews labeled as negative or positivewere obtained from Amazon.com dataset collections, and small datasets have been created. The Grey relational clusteringas well as hierarchical and partitional algorithms has been applied to the small datasets separately. According to the results,our model has better accuracy values than the other algorithms in clustering of small datasets containing short text. Consequently,we demonstrated that the Grey relational clustering should be applied to short text clustering for much better results.
机译：短文本聚类由于结构原因而面临巨大挑战，尤其是应用于小型数据集时。单词数量有限会导致特征向量质量差，聚类准确性低以及分析失败。尽管在相关文献中已经观察到一些方法，但是对于有效的解决方案仍未达成共识。另一方面，在数据不足的数值分析中给出更好结果的灰色系统理论尚未应用于短文本聚类。我们的研究目的是基于灰色系统理论开发适用于小型数据集的短文本聚类模型。为了衡量我们方法的效率，从Amazon.com数据集收集了标记为负面或正面的书评，并创建了小型数据集。灰色关系聚类以及分层和分区算法已分别应用于小型数据集。根据结果​​，在对包含短文本的小型数据集进行聚类时，我们的模型具有比其他算法更好的精度值。因此，我们证明了灰色关系聚类应该应用于短文本聚类以获得更好的结果。

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