Shared-nearest-neighbor-based clustering by fast search and find of density peaks

Song

Asso for Info Science & Tech

et al. 2019

Self Cite

Web ad effect evaluation is a challenging problem in web marketing research. Although the analysis of web ad effectiveness has achieved excellent results, there are still some deficiencies. First, there is a lack of an in-depth study of the relevance between advertisements and web content. Second, there is not a thorough analysis of the impacts of users and advertising features on user browsing behaviors. And last, the evaluation index of the web advertisement effect is not adequate. Given the above problems, we conducted our work by studying the observer's behavioral pattern based on multimodal features. First, we analyze the correlation between ads and links with different searching results and further assess the influence of relevance on the observer's attention to web ads using eye-movement features. Then we investigate the user's behavioral sequence and propose the directional frequent-browsing pattern algorithm for mining the user's most commonly used browsing patterns. Finally, we offer the novel use of "memory" as a new measure of advertising effectiveness and further build an advertising memory model with integrated multimodal features for predicting the efficacy of web ads. A large number of experiments have proved the superiority of our method.

Section: Resultsmentioning

confidence: 99%

Memory model for web ad effect based on multimodal features

Song

Asso for Info Science & Tech

et al. 2019

Self Cite

“…The weight parameters w L = ½w (1) L , …, w (m) L T and w D = ½w (1) D , …, w (n) D T added for each view guarantee that the objective functions in Eq. (12) and (13) adaptively learn an optimal consensus graph in terms of the importance of each view (Liu et al, 2018b). Finally, we integrate the optimization process from two spaces into one framework with graph-based multi-label learning and obtain the final objective function as follows:…”

Section: Multiview Consensus Graph Learning For Lncrna-disease Associmentioning

confidence: 99%

Multiview Consensus Graph Learning for lncRNA–Disease Association Prediction

Tan

Sun

et al. 2020

Front. Genet.

Long noncoding RNAs (lncRNAs) are a class of noncoding RNA molecules longer than 200 nucleotides. Recent studies have uncovered their functional roles in diverse cellular processes and tumorigenesis. Therefore, identifying novel disease-related lncRNAs might deepen our understanding of disease etiology. However, due to the relatively small number of verified associations between lncRNAs and diseases, it remains a challenging task to reliably and effectively predict the associated lncRNAs for given diseases. In this paper, we propose a novel multiview consensus graph learning method to infer potential disease-related lncRNAs. Specifically, we first construct a set of similarity matrices for lncRNAs and diseases by taking advantage of the known associations. We then iteratively learn a consensus graph from the multiple input matrices and simultaneously optimize the predicted association probability based on a multi-label learning framework. To convey the utility of our method, three state-of-the-art methods are compared with our method on three widely used datasets. The experiment results illustrate that our method could obtain the best prediction performance under different cross validation schemes. The case study analysis implemented for uterine cervical neoplasms further confirmed the utility of our method in identifying lncRNAs as potential prognostic biomarkers in practice.

“…It combines the advantages of both density-based and centroid-based clustering methods. Many variants have since been developed by using DP, such as gravitation-based density peaks clustering algorithm [33], FKNN-DPC [34] and SNN-DPC [35]. As a local density-based method, DP can obtain good results in most instances.…”

Section: Related Workmentioning

confidence: 99%

Clustering by Using the Way of Atomic Fission

Cheng

Mehmood

2020

IEEE Access

Cluster analysis which focuses on the grouping and categorization of similar elements is widely used in various fields of research. Inspired by the phenomenon of atomic fission, a novel density-based clustering algorithm is proposed in this paper, called fission clustering (FC). It focuses on mining the dense families of a dataset and utilizes the information of the distance matrix to fissure clustering dataset into subsets. When we face the dataset which has a few points surround the dense families of clusters, K-nearest neighbors local density indicator is applied to distinguish and remove the points of sparse areas so as to obtain a dense subset that is constituted by the dense families of clusters. A number of frequently-used datasets were used to test the performance of this clustering approach, and to compare the results with those of algorithms. The proposed algorithm is found to outperform other algorithms in speed and accuracy.