An ontology-based and domain specific clustering methodology for financial documents

Kulathunga, Chalitha; Karunaratne, D. D.

doi:10.1109/icter.2017.8257786

Cited by 7 publications

(4 citation statements)

References 10 publications

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“…In [41] paper the approach named (An Ontology-based and Domain Specific Clustering Methodology for Financial Documents). The main steps in approach are (preprocessing and feature extraction, sense word disambiguation, representation document and clustering).…”

Section: Literature Reviewmentioning

confidence: 99%

Survey on Semantic Similarity Based on Document Clustering

Ibrahim¹,

Zeebaree²,

Jacksi³

2019

Adv. sci. technol. eng. syst. j.

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Clustering is a branch of data mining which involves grouping similar data in a collection known as cluster. Clustering can be used in many fields, one of the important applications is the intelligent text clustering. Text clustering in traditional algorithms was collecting documents based on keyword matching, this means that the documents were clustered without having any descriptive notions. Hence, non-similar documents were collected in the same cluster. The key solution for this problem is to cluster documents based on semantic similarity, where the documents are clustered based on the meaning and not keywords. In this research, fifty papers which use semantic similarity in different fields have been reviewed, thirteen of them that are using semantic similarity based on document clustering in five recent years have been selected for a deep study. A comprehensive literature review for all the selected papers is stated. A comparison regarding their algorithms, used tools, and evaluation methods is given. Finally, an intensive discussion comparing the works is presented.

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Section: Literature Reviewmentioning

confidence: 99%

Survey on Semantic Similarity Based on Document Clustering

Ibrahim¹,

Zeebaree²,

Jacksi³

2019

Adv. sci. technol. eng. syst. j.

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“…Design Ontology PLKB has a purpose of developing an Ontology model of family planning that integrates the various types family planning entity with data obtained that is Counseling, Contraception, and Planning based on existing data on Family Planning Field Officer [9]. Also, the data obtained can be done integration and classification with Web Ontology [10].…”

Section: Related Workmentioning

confidence: 99%

Ontology design based on data family planning field officer using OWL and RDF

Awangga¹,

Assegaff²,

Pane³

et al. 2019

TELKOMNIKA

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Population density in Indonesia is ranked fourth in the world. The impact of a large population will affect the level of welfare of the community to decrease, and the number of unemployment is increasing so that the state makes Family Planning Program (PLKB) to control the rate of population growth. Problems in the PLKB program are on knowledge management and mapping from data contraception, counseling and planning so that this research using Ontology method will aim to do mapping with knowledge management and Ontology design shows represented data to relate and describes the resources contained in family planning data. This research approach the representation of ontology that is validated through model transformation from family planning data to ontology design using OWL and RDF which are useful for data processing and representing data to be utilized by field officers in educating the public and eradicating negative issues about family planning programs.

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“…Performance improved for the micro-cluster based system used the idea of a shared density chart that catches the density of the existing information within micro clusters during cauterization and showing how can graph used for re-clustering micro-clusters to reduce the density. Survey of the data stream clustering algorithms [20] An advantage over several areas such as market analysis, crime detection, also, this paper presents a survey of the usually-employed empirical methodologies. The study describes a summary of the data stream clustering problems.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Displays the performance analysis of the proposed system with a different flavor of current classification algorithms. according to RF, NB [20], ANN [21] statistics the RNN framework provides better reliability for the identification of structured data as it gives a minimum failure rate for the whole data set during the classification.…”

Section: Fig 5 System Processing Timementioning

confidence: 99%

Micro Clustering Methodology for Document Objects using Deep Learning Techniques

Mulla,

Patil

2019

IJEAT

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Large data clustering and classification is a very challenging task in data mining. Various machine learning and deep learning systems have been proposed by many researchers on a different dataset. Data volume, data size and structure of data may affect the time complexity of the system. This paper described a new document object classification approach using deep learning (DL) and proposed a recurrent neural network (RNN) for classification with a micro-clustering approach.TF-IDF and a density-based approach are used to store the best features. The plane work used supervised learning method and it extracts features set called as BK of the desired classes. once the training part completed then proceeds to figure out the particular test instances with the help of the planned classification algorithm. Recurrent Neural Network categorized the particular test object according to their weights. The system can able to work on heterogeneous data set and generate the micro-clusters according to classified results. The system also carried out experimental analysis with classical machine learning algorithms. The proposed algorithm shows higher accuracy than the existing density-based approach on different data sets.

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An ontology-based and domain specific clustering methodology for financial documents

Cited by 7 publications

References 10 publications

Survey on Semantic Similarity Based on Document Clustering

Survey on Semantic Similarity Based on Document Clustering

Ontology design based on data family planning field officer using OWL and RDF

Micro Clustering Methodology for Document Objects using Deep Learning Techniques

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