Summarization using mapreduce framework based big data and hybrid algorithm (HMM and DBSCAN)

Belerao, Krushnadeo; Chaudhari, S. B.

doi:10.1109/icpcsi.2017.8392320

Cited by 3 publications

(2 citation statements)

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“…Density-based clustering is a robust algorithm in analyzing specific data with a most remarkable performance [30], and it can provide adequate security in clustering data at various distributed datasets [31][32][33]. Document clustering has been used in many applications like text summarization [23,24,34], key phrase detection [35], and topic modeling [36,37]. A literature review regarding the related works about GloVe word embedding and density-based clustering algorithms presented in the next two sections.…”

Section: Literature Reviewmentioning

confidence: 99%

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A state-of-the-art survey on semantic similarity for document clustering using GloVe and density-based algorithms

Mohammed

Jacksi

Zeebaree

2021

IJEECS

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<p><span>Semantic similarity is the process of identifying relevant data semantically. The traditional way of identifying document similarity is by using synonymous keywords and syntactician. In comparison, semantic similarity is to find similar data using meaning of words and semantics. Clustering is a concept of grouping objects that have the same features and properties as a cluster and separate from those objects that have different features and properties. In semantic document clustering, documents are clustered using semantic similarity techniques with similarity measurements. One of the common techniques to cluster documents is the density-based clustering algorithms using the density of data points as a main strategic to measure the similarity between them. In this paper, a state-of-the-art survey is presented to analyze the density-based algorithms for clustering documents. Furthermore, the similarity and evaluation measures are investigated with the selected algorithms to grasp the common ones. The delivered review revealed that the most used density-based algorithms in document clustering are DBSCAN and DPC. The most effective similarity measurement has been used with density-based algorithms, specifically DBSCAN and DPC, is Cosine similarity with F-measure for performance and accuracy evaluation.</span></p>

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Section: Literature Reviewmentioning

confidence: 99%

“…It means that the primary notion of this approach is the density of objects. In the literature, there are many types of density-based algorithms, and the newest that has been used extensively is DBSCAN [22][23][24], and DPC [25][26][27].…”

Section: Introductionmentioning

confidence: 99%