Now-a-days, data is the vital component of all the worldly activities. Almost all the domains such as finance, healthcare, retail, banking and marketing maintain data that are collected from the service consumers. Data mining is organizing and retrieving information from large data set. Data mining searches large stores of data for discovering patterns and trends for simple analysis. It uses sophisticated mathematical algorithms to partition the data. Identification of similar and dissimilar attribute is a challenging task. Text clustering is one of the most researchable topics, owing to the massive usage of textual documents. The aim of text clustering algorithms is to group similar textual documents together, which can improve the data organization and makes the process of data analysis simpler. Clustering algorithms identifies the similarity between the elements in the given data set. The aim of clustering is to find intrinsic structures in data and organize them into meaningful subgroups for further study and analysis. Understanding the advantages of data clustering and the prevalent usage of textual data, this research sets its goal to present a textual data clustering algorithms. A detailed study has been performed and from the literature, it is observed that these methods are have limitations for the different data analysis and are inefficient in data storage and retrieval. Therefore in this research work, textual data clustering is applied to overcome the data analysis drawbacks and to achieve better textual clusters. To analyze and improve the data organization efficiently and to attain better clusters accuracy, the optimized textual clustering algorithms are proposed. Three phases are structured for implementation such as Pre-processing, Similarity computation and Text clustering. In the first contribution, the initial cluster points by means of LOA and the clusters are enhanced by means of k-means algorithm. The proposed algorithm acquires the advantages of both the LOA and k-means algorithm. The computation of the similarity level between the documents and is accomplished by the cosine similarity measure. The success probability is computed by taking the fitness value ranges from 0 to 1 and it indicates the similarity between the textual data and the centroid of the cluster. The pride of lions is arranged in ascending order with respect to the fitness value and the lions with minimal similarity are discarded. K-means algorithm is applied to enhance the so formed cluster.