Clustering a large number of short texts in social media has great value in applications.However

short texts often have these characteristics:lots of noises

growing rapidly and massive data.Most existing short text clustering algorithms are not effectively enough to process such short texts.Aiming at this problem

we propose an algorithm of short text online clustering based on incremental robust nonnegative matrix factorization (STOCIRNMF).This algorithm uses NMF to build the short text clustering model and applies

l

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norm to devise its objective function for improving its robustness.Meanwhile

STOCIRNMF can cluster short texts incrementally by using incremental iterative update rules.We co

nduct extensive experiments on real Sohu news titles and Weibo datasets.The results show that STOCIRNMF not only has better performance of short text clustering than some representative algorithms

but also is very effective to detect micro blog's topics online.