Semi-supervised document clustering using Seeds affinity propagation and consensus algorithm in multi-domain settings

High-grade ovarian carcinoma is a histopathological diagnosis, however, at the molecular level, ovarian cancer represents a heterogeneous group of diseases. Studies aimed at identifying molecular genetic subtypes of ovarian cancer are conducted in order to find the answer to the question: can different molecular subgroups influence the choice of treatment? One of the achievements in this trend is the recognition of the dualistic model that categorizes various types of ovarian cancer into two groups designated high-grade (HG) and low-grade (LG) tumors. However, the tumor genome sequencing data suggest the existence of 6 ovarian carcinoma subtypes, including two LG and four HG subtypes. Subtype C1 exhibits a high stromal response and the lowest survival. Subtypes C2 and C4 demonstrate higher number of intratumoral CD3 + cells, lower stromal gene expression and better survival than sybtype C1. Subtype C5 (mesenchymal) is characterized by mesenchymal cells, over-expression of N-cadherin and P-cadherin, low expression of differentiation markers, and lower survival rates than C2 and C4. The use of a consensus algorithm to determine the subtype allows identification of only a minority of ovarian carcinomas (approximately 25%) therefore, the practical importance of this classification requires additional research. There is evidence that it makes sense to randomize tumors into groups with altered expression of angiogenic genes and groups with overexpression of the immune response genes, as in the angiogenic group there is a comparative superiority in terms of survival. The administration of bevacizumab in the angiogenic group improves survival, while the administration of bevacizumab in the immune group even worsens the outcome. Molecular subtypes with worse survival rates (proliferative and mesenchymal) also benefit most from bevacizumab treatment. This review focuses on some of the advances in understanding molecular, cellular, and genetic changes in ovarian carcinomas with the results achieved so far regarding the formulation of molecular subtypes of ovarian cancer, however further studies are needed.

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Comparision of Different Distance Measure Methods in Text Document Clustering

INTERNATIONAL JOURNAL OF RESEARCH AND ENGINEERING ◽

10.21276/ijre.2018.5.7.2 ◽

2018 ◽

Vol 5 (7) ◽

Author(s):

Yin Min Tun ◽

Keyword(s):

Distance Measure ◽

Document Clustering ◽

Text Document ◽

Measure Methods

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E-Voting System Using Proof of Voting (PoV) Consensus Algorithm Using Block Chain Technology

SSRN Electronic Journal ◽

10.2139/ssrn.3729053 ◽

2018 ◽

Author(s):

Ketulkumar Govindbhai Chaudhari

Keyword(s):

Consensus Algorithm ◽

Voting System ◽

Block Chain

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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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