Enhancing web search result clustering model based on multiview multirepresentation consensus cluster ensemble (mmcc) approach

Abstract

Existing text clustering methods utilize only one representation at a time (single view), whereas multiple views can represent documents. The multiview multirepresentation method enhances clustering quality. Moreover, existing clustering methods that utilize more than one representation at a time (multiview) use representation with the same nature. Hence, using multiple views that represent data in a different representation with clustering methods is reasonable to create a diverse set of candidate clustering solutions. On this basis, an effective dynamic clustering method must consider combining multiple views of data including semantic view, lexical view (word weighting), and topic view as well as the number of clusters. The main goal of this study is to develop a new method that can improve the performance of web search result clustering (WSRC). An enhanced multiview multirepresentation consensus clustering ensemble (MMCC) method is proposed to create a set of diverse candidate solutions and select a high-quality overlapping cluster. The overlapping clusters are obtained from the candidate solutions created by different clustering methods. The framework to develop the proposed MMCC includes numerous stages: (1) acquiring the standard datasets (MORESQUE and Open Directory Project-239), which are used to validate search result clustering algorithms, (2) preprocessing the dataset, (3) applying multiview multirepresentation clustering models, (4) using the radius-based cluster number estimation algorithm, and (5) employing the consensus clustering ensemble method. Results show an improvement in clustering methods when multiview multirepresentation is used. More importantly, the proposed MMCC model improves the overall performance of WSRC compared with all single-view clustering models.

Molecular Characteristics, Clinical Significance, and Cancer Immune Interactions of Angiogenesis-Associated Genes in Gastric Cancer

“…Consensus clustering was employed to define distinct angiogenesis-related patterns by the k-means algorithms ( 19 ). The quantity, as well as consistency of clusters, were built by the consensus clustering algorithm, which is available in the “ConsensuClusterPlus” package ( 20 ).…”

A novel angiogenesis-based molecular signature related to prognosis and tumor immune interactions of pancreatic cancer

“…Initially, the univariate Cox regression analysis was employed to assess the prognostic values of 36 ARGs obtained from the MSigDB Team (Hallmark Gene set) in PC patients, then, p < 0.05 was selected as a screening threshold, and 11 prognostic ARGs were screened out with univariate Cox analysis ( p < 0.05). Based on the expression level of these ARGs, 428 PC cases from TCGA, ICGC, and GTEx cohorts were divided into two distinct subtypes by the consensus clustering analysis with “ConsensusClusterPlus R package” ( Seiler et al, 2010 ; Sabah et al, 2021 ).…”

“…Based on the expression level of these ARGs, 428 PC cases from TCGA, ICGC, and GTEx cohorts were divided into two distinct subtypes by the consensus clustering analysis with "ConsensusClusterPlus R package" (Seiler et al, 2010;Sabah et al, 2021).…”

Construction of a mitochondrial dysfunction related signature of diagnosed model to obstructive sleep apnea

“…Using all the OSA and control samples, a consensus clustering analysis of mitochondrial dysfunction-related genes was used to identify distinct mitochondrial dysfunction-related clusters using the k-means clustering algorithm ( Sabah et al, 2021 ). The optimum number of clusters, along with the consistency of clusters, was determined by the consensus clustering algorithm in the ConsensusClusterPlus package ( Seiler et al, 2010 ).…”