Abstract
One of the fastest growing research fields in recent times is Document Clustering. It gained its importance in text mining due to the tremendous increase of documents on internet. Textual data management is the need of every organization and clustering the documents is one of the fastest and widely used techniques. Document clustering is an unsupervised technique that organizes similar documents into classes in order to improve information retrieval. The overall evaluation of the research work is performed by comparing the working procedure and merits of each method with other in terms of some performance metrics. This research work concluded with the better research method which can be applied to cluster the similar documents on the basis of text. The comparative analysis shows the accuracy of documents clustered using K-Means are high compare to other approaches of unsupervised learning in terms of F-Measure, Precision, recall and time complexity.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Abhilash CB, Basavanagowda S (2013) A comparative study on clustering of data using improved K- means algorithms. Int J Comput Trends Technol (IJCTT), 4(4). ISSN: 2231-2803
Huang A Similarity measures for text document clustering, Department of Computer Science
Aggarwal CC, Zhai CX A survey of text clustering algorithms. IBM T J Watson Res Cent Yorktown Heights
Polat K (2011) Application of attribute weighting method based on clustering centers to discrimination of linearly non-separable medical datasets. J. Med Syst
Hammouda KM, Kamel MS (2004) Efficient phrase-based document indexing for web document clustering. IEEE Trans Knowl Data Eng 16(10)
Ribeiro MN, Neto MJR, Prudˆencio RBC Local feature selection in text clustering, Universidade Federal de Pernambuco
Law MHC, Figueiredo MAT, Jain AK (2004) Simultaneous feature selection and clustering using mixture models. IEEE Trans Pattern Anal Mach Intell 6(9)
khairnar V, Patil S (2016) Efficient clustering of data using improved K-means algorithm: a review. Imperial J Interdisc Res (IJIR), 2(1). ISSN 2454-1362
Soni N, Ganatra A (2012) Categorization of several clustering algorithms from different perspective: a review. Int J Adv Res Comput Sci Softw Eng, 2(8). ISSN 2277 128X
Singh P, Sharma M (2013) Text document clustering and similarity measures, Department of Computer Science and Engineering
Alelyani S, Tang J, Liu H Feature selection for clustering: a review
Kunwar S (2013) Text documents clustering using K- means algorithm
Sharmila P, Shanthalakshmi Revathy J (2013) An efficient clustering algorithm for spam mail detection. Int J Adv Res Comput Sci Softw Eng 3(3). ISSN 2277 128X
Kolhe S, Sawarkar S (2015) Review of document clustering techniques: issues, challenges and feasible avenue. Int J Adv Res Comput Sci Softw Eng, 5(4). ISSN 2277 128X
Bisht S, Paul A (2013) Document clustering: a review. Int J Comput Appl 73(11)
Mori T, Kikuchi M, Yoshida K Term weighting method based on information gain ratio for summarizing documents retrieved by IR systems. Div Electr Comput Eng
Yafooz WMS, Abidin SZZ, Omar N, Halim RA (2013) Dynamic semantic textual document clustering using frequent terms and named entity. In: IEEE 3rd international conference on system engineering and technology
Jain Y, Nandanwar AK (2014) A theoretical study of text document clustering. Int J Comput Sci Inf Technol 5(2):2246–2251. ISSN 0975 9646
https://kdd.ics.uci.edu/databases/20newsgroups/20newsgroups.html
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Afreen, M., Badugu, S. (2020). Document Clustering Using Different Unsupervised Learning Approaches: A Survey. In: Satapathy, S.C., Raju, K.S., Shyamala, K., Krishna, D.R., Favorskaya, M.N. (eds) Advances in Decision Sciences, Image Processing, Security and Computer Vision. ICETE 2019. Learning and Analytics in Intelligent Systems, vol 4. Springer, Cham. https://doi.org/10.1007/978-3-030-24318-0_71
Download citation
DOI: https://doi.org/10.1007/978-3-030-24318-0_71
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-24317-3
Online ISBN: 978-3-030-24318-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)