A GRAPH BASED TEXT DOCUMENT CLUSTERING USING HARRIS HAWKS OPTIMIZER

The proposed methodology employs Harris hawks optimizer algorithm for text document clustering. The main influence of Harris hawks optimizer algorithm is the collective character and style of persecution of the wild Harris's hawk, which is also considered to be seven killing methods called surprise attacks. Clustering of documents is a task of grouping a document automatically into a list of meaningful clusters in order for the documents inside a group to share the same topic. First, the keyphrases are extracted from each document with its frequency count using graph based keyphrase extraction approach. With the aid of the top listed keyphrases, the documents clustering are carried out by implementing the proposed Harris hawks optimizer algorithm. The simulation results reveals that the proposed graph based text document clustering using Harris hawks optimizer obtained the best results for clustering the text documents.