Semantic based Document Clustering: A Detailed Review

Shah, Neepa; Mahajan, Sunita

doi:10.5120/8202-1598

Cited by 16 publications

(14 citation statements)

References 27 publications

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“…Getting from an initial collection of documents to a clustering of the collection is an elaborate procedure, which usually involves several stages. The basic operations are feature extraction and selection, document representation and clustering [4]. Feature extraction is usually the first step of the process and filters out non-appropriate words from the documents' descriptions.…”

Section: Topic Modeling In Document Clusteringmentioning

confidence: 99%

“…The effectiveness of the FBHC was examined using the external metrics TS and FScore, and comparing the results with baseline hierarchical clustering algorithms implemented in R language. Both division (Diana) 3 and agglomerative (Average, Single, Complete and Ward) 4 hierarchical clustering algorithms were used as baselines.…”

Section: Effectiveness Evaluationmentioning

confidence: 99%

“…Some of the current problems with document clustering [4] include the selection of appropriate document features and similarity measures, the quality assessment of the clusters, the implementation of an efficient clustering algorithm which can make optimal use of the available memory and CPU resources, the association of meaningful labels to the final clusters, and the consideration of the semantic relationships between words. Hierarchical document clustering methods have to deal with additional challenges, including the handling of the very high dimensionality of the data.…”

Section: Introductionmentioning

confidence: 99%

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A dockerized framework for hierarchical frequency-based document clustering on cloud computing infrastructures

2020

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Scalable big data analysis frameworks are of paramount importance in the modern web society, which is characterized by a huge number of resources, including electronic text documents. Document clustering is an important field in text mining and is commonly used for document organization, browsing, summarization and classification. Hierarchical clustering methods construct a hierarchy structure that, combined with the produced clusters, can be useful in managing documents, thus making the browsing and navigation process easier and quicker, and providing only relevant information to the users' queries by leveraging the structure relationships. Nevertheless, the high computational cost and memory usage of baseline hierarchical clustering algorithms render them inappropriate for the vast number of documents that must be handled daily. In this paper, we propose a new scalable hierarchical clustering framework, which uses the frequency of the topics in the documents to overcome these limitations. Our work consists of a binary tree construction algorithm that creates a hierarchy of the documents using three metrics (Identity, Entropy, Bin Similarity), and a branch breaking algorithm which composes the final clusters by applying thresholds to each branch of the tree. The clustering algorithm is followed by a meta-clustering module which makes use of graph theory to obtain insights in the leaf clusters' connections. The feature vectors representing each document derive from topic modeling. At the implementation level, the clustering method has been dockerized in order to facilitate its deployment on cloud computing infrastructures. Finally, the proposed framework is evaluated on several datasets of varying size and content, achieving significant reduction in both memory consumption and computational time over existing hierarchical clustering algorithms. The experiments also include performance testing on cloud resources using different setups and the results are promising.

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Section: Topic Modeling In Document Clusteringmentioning

confidence: 99%

Section: Effectiveness Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A dockerized framework for hierarchical frequency-based document clustering on cloud computing infrastructures

2020

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“…In this section, we present a survey of some approaches in documents clustering. Shah and Mahajan (2012) presented a detailed review of semantic based documents clustering. Roul (2018) proposed an approach that consists of clustering engine search results and ranking results of each cluster.…”

Section: Related Workmentioning

confidence: 99%

Automatic generation of ontologies: a hierarchical word clustering approach

Sellah¹,

Hilaire²

2018

IJCSIS

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In the context of globalization, companies need to capitalize on their knowledge. The knowledge of a company is present in two forms tacit and explicit. Explicit knowledge represents all formalized information i.e all documents (pdf, words ...). Tacit knowledge is present in documents and mind of employees, this kind of knowledge is not formalized, it needs a reasoning process to discover it. The approach proposed focus on extracting tacit knowledge from textual documents. In this paper, we propose hierarchical word clustering as an improvement of word clusters generated in previous work, we also proposed an approach to extract relevant bigrams and trigrams. We use Reuters-21578 corpus to validate our approach. Our global work aims to ease the automatic building of ontologies.

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“…The issues of these methods are: [2]. In [3] the author presented a brief survey on various clustering techniques based on semantics. In [4] the authors proposed a semantic text document clustering approach based on the WordNet lexical categories and Self Organizing Map (SOM) neural network.…”

Section: Literature Reviewmentioning

confidence: 99%

Dynamic Clustering of Documents

.¹

2015

IJRET

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Due to increasing number of documents in the World Wide Web, which requires faster processing and efficient methods to handle them retrieval has become a major issue. Clustering process is used to retrieve the document and automatically group them into clusters. Here the issue is scalability. Therefore to handle the scalability problems documents are clustered dynamically when they add up, without the need to re-cluster the formulated clusters which are clustered by static document clustering. The proposed method will dynamically process the newly arrived document and assign it to the meaningful cluster without the need to disturb the formed clusters. In order to exploit semantic relation between terms within the document WordNet ontology is been used to add hypernyms.

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Semantic based Document Clustering: A Detailed Review

Cited by 16 publications

References 27 publications

A dockerized framework for hierarchical frequency-based document clustering on cloud computing infrastructures

A dockerized framework for hierarchical frequency-based document clustering on cloud computing infrastructures

Automatic generation of ontologies: a hierarchical word clustering approach

Dynamic Clustering of Documents

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