CSMR: A Scalable Algorithm for Text Clustering with Cosine Similarity and MapReduce

Chen (2018) proposed a scalable spectral clustering algorithm for cosine similarity to handle the task of clustering large data sets. It runs extremely fast, with a linear complexity in the size of the data, and achieves state of the art accuracy. This paper conducts perturbation analysis of the algorithm to understand the effect of discarding a perturbation term in an eigendecomposition step. Our results show that the accuracy of the approximation by the scalable algorithm depends on the connectivity of the clusters, their separation and sizes, and is especially accurate for large data sets.

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K-means text clustering algorithm based on density and nearest neighbor

Journal of Computer Applications ◽

10.3724/sp.j.1087.2010.01933 ◽

2010 ◽

Vol 30 (7) ◽

pp. 1933-1935 ◽

Cited By ~ 6

Author(s):

Wen-ming ZHANG ◽

Jiang WU ◽

Xiao-jiao YUAN

Keyword(s):

Clustering Algorithm ◽

Nearest Neighbor ◽

Text Clustering

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An Improved B-hill Climbing Optimization Technique for Solving the Text Documents Clustering Problem

Current Medical Imaging Formerly Current Medical Imaging Reviews ◽

10.2174/1573405614666180903112541 ◽

2020 ◽

Vol 16 (4) ◽

pp. 296-306 ◽

Cited By ~ 3

Author(s):

Laith Mohammad Abualigah ◽

Essam Said Hanandeh ◽

Ahamad Tajudin Khader ◽

Mohammed Abdallh Otair ◽

Shishir Kumar Shandilya

Keyword(s):

Optimization Technique ◽

Document Clustering ◽

Text Clustering ◽

Hill Climbing ◽

Text Documents ◽

Clustering Problem ◽

Text Document ◽

Text Information ◽

Amount Of Knowledge ◽

The Hill

Background: Considering the increasing volume of text document information on Internet pages, dealing with such a tremendous amount of knowledge becomes totally complex due to its large size. Text clustering is a common optimization problem used to manage a large amount of text information into a subset of comparable and coherent clusters. Aims: This paper presents a novel local clustering technique, namely, β-hill climbing, to solve the problem of the text document clustering through modeling the β-hill climbing technique for partitioning the similar documents into the same cluster. Methods: The β parameter is the primary innovation in β-hill climbing technique. It has been introduced in order to perform a balance between local and global search. Local search methods are successfully applied to solve the problem of the text document clustering such as; k-medoid and kmean techniques. Results: Experiments were conducted on eight benchmark standard text datasets with different characteristics taken from the Laboratory of Computational Intelligence (LABIC). The results proved that the proposed β-hill climbing achieved better results in comparison with the original hill climbing technique in solving the text clustering problem. Conclusion: The performance of the text clustering is useful by adding the β operator to the hill climbing.

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