With the boom of web and social network, the amount of generated text data has increased enormously. On one hand, although text clustering methods are applicable to classify text data and facilitate data mining work such as information retrieval and recommendation, inadequate aspects are still evident. Especially, most existing text clustering methods provide either a hard partitioned or a hierarchical result, which cannot describe the data from various perspectives. On the other hand, multiple clustering approaches, which are proposed to classify data with various perspectives, meet several challenges such as high time complexity and incomprehensible results while applied to text documents. In this paper, we propose a frequent term-based multiple clustering approach for text documents. Our approach classifies text documents with various perspectives and provides a semantic explanation for each cluster. Through a series of experiments, we prove that our method is more scalable and provides more comprehensible results than traditional multiple clustering methods such as OSCLU and ASCLU while applied to text documents. In addition, we also found that our approach achieves a better clustering quality than existing text clustering approaches like FTC.
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