The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
An Evidence-Based Spectral Clustering (EBSC) algorithm that works well for data with mixed numeric and nominal features is presented. A similarity measure based on evidence accumulation is adopted to define the similarity measure between pairs of objects, which makes no assumptions of the underlying distributions of the feature values. A spectral clustering algorithm is employed on the similarity...
Clustering ensembles have emerged as a powerful method for improving both the robustness and the stability of unsupervised classification solutions. However, finding a consensus clustering from multiple partitions is a difficult problem that can be approached from graph-based, combinatorial or statistical perspectives. A consensus scheme via the k-modes algorithm is proposed in this paper. A combined...
In this paper, a pipelined implementation of a Hessenberg-based input balanced realization is derived, which is realized by placing the delay elements in the lower path and the upper path alternately. The proposed pipelined structure is canonical in the sense that no extra delay is needed. For an Nth order filter, the proposed structure requires 5N −1 multipliers, which has two less multipliers than...
Availability of large-scale network data for real systems is enabling mathematical and computational methods to systematically model the formation of the networks. Various growth models are proposed to reproduce the structures of the real-world networks. Evaluating how well a model fits the network data is an outstanding challenge, since the structures of networks that have tens of thousands of vertices...
We propose a simplified model which exhibits community structure, power-law degree distribution and high clustering. Every vertex is a social one with a social identity. The preferential attachment of Barabasi-Albert model is incorporated with social similarity. When a newly added vertex makes a new link, it first selects a certain group of vertices with a probability by considering the social distances...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.