• Manpreet kaur
• vandana

The usage unsupervised clustering approaches in numerous applications has attracted the researchers as an innovative research area due to separation characteristics of unstructured format of documents in a similar group contains same data. Increasing requirement in numerous research fields and information technologies, led to an increase in the document clustering approaches with maximum efficiency. Therefore, in exiting works, researchers take a lot of time to find similar documents according to the requirement. During the document clustering, several types of issues faced by the researchers like best centroid localization, less uniqueness of clustered data, high clustering time and many more. To minimize these types of problems, most of the previous research focuses on similarity techniques for clustering but the clustering results are not satisfactory. In this research article, we developed an improved K-means based text document clustering using Artificial Bee Colony (ABC) along with the Support Vector Machine (SVM) as a concept of unsupervised mechanism. Here, SVM act as a machine learning technique and helps to classify the best centroid according to optimized centroid by K-means with ABC. Based on this mechanism, proposed work achieved better clustering performance that is validated by comparing with existing works based on the Accuracy, Normalized mutual information (NMI) and Adjusted Rand index (ARI)

Document Clustering, Data Mining, Improved K-means, Artificial Bee Colony Similarity, Support Vector Machine, Performance Parameters

"An Improved K-means based Text Document Clustering using Artificial Bee Colony with Support Vector Machine", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 7, page no.c43-c49, July-2021, Available :http://www.jetir.org/papers/JETIR2107270.pdf

"An Improved K-means based Text Document Clustering using Artificial Bee Colony with Support Vector Machine", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 7, page no. ppc43-c49, July-2021, Available at : http://www.jetir.org/papers/JETIR2107270.pdf