The vast amount of textual information available in electronic form is growing at a staggering rate in recent times. The task of mining useful or interesting frequent itemsets (words/terms) from very large text databases that are formed as a result of the increasing number of textual data still seems to be a quite challenging task. A great deal of attention in research community has been received by the use of such frequent itemsets for text clustering, because the dimensionality of the documents is drastically reduced by the mined frequent itemsets. Based on frequent itemsets, an efficient approach for text clustering has been devised. For mining the frequent itemsets, a renowned method, called Apriori algorithm has been used. Then, the documents are initially partitioned without overlapping by making use of mined frequent itemsets. Furthermore, by grouping the documents within the partition using derived keywords, the resultant clusters are obtained effectively. In this paper, we have presented an extensive analysis of frequent itemset-based text clustering approach for different real life datasets and the performance of the frequent itemset-based text clustering approach is evaluated with the help of evaluation measures such as, precision, recall and F-measure. The experimental results shows that the efficiency of the frequent itemset-based text clustering approach has been improved significantly for different real life datasets.