Received: November 11, 2018 278 International Journal of Intelligent Engineering and Systems, Vol.12, No.2, 2019 DOI: 10.22266/ijies2019.0430.27 Text Clustering Algorithm Using Fuzzy Whale Optimization Algorithm Jagatheeshkumar Gopal 1 * Selva Brunda 2 1 Research and Development Centre, Bharathiar University, Coimbatore, India 2 Department of Computer Science and Engineering, Cheran College of Engineering, Karur, India * Corresponding author’s Email: jagatheeshkumar.bu@gmail.com Abstract: 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. Understanding the benefits, this article intends to present a text clustering algorithm that is based on Fuzzy C Means (FCM) and Whale Optimization Algorithm (WOA). The optimization algorithm (WOA) selects the cluster centre, which helps the FCM to arrive at better textual clusters. The performance of the proposed text clustering algorithm is tested in terms of precision, recall, f- measure, purity and entropy over three different benchmark datasets. The performance of the proposed algorithm is observed to be reasonable when compared to the existing algorithms with an average F-measure of 97.6%. Keywords: Text clustering, FCM, WOA, Optimization algorithm. 1. Introduction Data is the quintessential part of all the live transactions. These data are beneficial and are stored for future analysis. Initially, the transactional data are stored in memory for future references, however the data analysis is not simple in this case. The main reasons for the difficult data analysis are the inefficient data storage and retrieval. These drawbacks are addressed rapidly by the modernized storage techniques. Due to the advancement of storage techniques and technology, the knowledge based systems are developed for achieving efficient data storage and retrieval. The knowledge based systems work intelligently by means of the incorporated techniques. A knowledge based system can achieve two basic tasks, which are clustering and classification. The process of clustering attempts to group similar data being processed and the process of classification aim to distinguish between the entities in the dataset. The clustering and classification activities are called as unsupervised and supervised learning techniques respectively. The process of classification is termed as supervised learning, as the classification system necessitates the training phase. The objective of training phase is to equip the classifier with the knowledge extracted from the training samples. The classifier is then suitable for classifying between the entities. On the other hand, the process of clustering does not require any training phase and it works on the go. Hence, the clustering operation is termed as unsupervised and it works without any prior activities. As the related data items are grouped together, the data can be analysed without any hassles. Understanding the advantages of data clustering and the prevalent usage of textual data, this work sets its goal to present a textual data clustering algorithm by combining the Fuzzy C Means (FCM) and Whale Optimization Algorithm (WOA) presented in [1]. FCM algorithm is closely associated with k-means algorithm but is based on the fuzzy theory. Several improvised versions of FCM are proposed in the literature for attaining better clustering results. The traditional FCM algorithm fixes the count of clusters before the start of the clustering process [2]. In addition to this, a clustering rule that implies the data item with a specific dimension has to be placed