A survey of clustering ensemble algorithms. Interna- tional Journal of Pattern Recognition and Artificial Intelligence, 25(03):337-372, 2011.Vega-Pons S, Ruiz-Shulcloper J, A Survey of Clustering Ensemble Algorithms, International Jour- nal of Pattern Recognition and Artificial Intelligence, 2011,...
This survey has presented classical and recently developed approaches to cluster ensemble. It kicks off with the formal terminology by which the problem is defined. Four basic categories of consensus clustering methods are then discussed in depth with illustrative examples. After that, it provides deta...
The basic idea of this kind of clustering algorithms is to construct the hierarchical relationship among data in order to cluster [16]. Suppose that each data point stands for an individual cluster in the beginning, and then, the most neighboring two clusters are merged into a new cluster unti...
In this survey, we turn to investigate the concepts and limitations of unimodal identity recognition, the motivation, and advantages of multimodal identity recognition, and summarize the recognition technologies and applications via feature level, match score level, decision level, and rank level data ...
“Blockchain technology” provides an overview of blockchain with explanations of Ethereum, smart contracts and consensus algorithms. “Machine learning” describes machine learning. “Literature review” provides the literature overview on the integration of these two technologies, along with their ...
“Amnesia” - A Selection of Machine Learning Models That Can Forget User Data Very Fast CIDR 2019 Humans forget, machines remember: Artificial intelligence and the Right to Be Forgotten Computer Law & Security Review 2018 Algorithms that remember: model inversion attacks and data protection law Phi...
namely multilayer networks. Communities in a single-layer network are groups of nodes that are more strongly connected among themselves than the others, while in multilayer networks, a group of well-connected nodes are shared in multiple layers. Most traditional algorithms can rarely perform well on...
This directly leads to limited sizes and diversity in public point-cloud datasets as shown in Table I, and poses a great challenge while developing generalizable point cloud learning algorithms. Studying label-efficient point cloud learning has become an urgent need to mitigate the limitation of ...
Subspace outlier detection has emerged as a practical approach for outlier detection. Classical full space outlier detection methods become ineffective in high dimensional data due to the “curse of dimensionality”. Subspace outlier detection methods ha
We then present three main categories of CF techniques: memory-based, model-based, and hybrid CF algorithms (that combine CF with other recommendation techniques), with examples for representative algorithms of each category, and analysis of their predictive performance and their ability to address ...