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.
Multi-view clustering and clustering ensembles have become increasingly popular in recent years. Multi-view clustering employs the relationship between views to cluster data; clustering ensembles combine different component c1usterings to a better final partition. In this paper, we proposed the multi-view clustering ensembles which extend clustering ensembles to multi-view clustering. Experimental...
Smartphone becomes more popular recently. Much important information is stored and processed in the Smartphones. It attracts the attention of hackers. Malware is one of the most common security issues in Smartphone, especially for the Android system due to its compatibility. In this paper, we focus on the Android malicious application problem. As malwares with different purposes have different properties,...
Diagrams are a straightforward knowledge-representation approach to reveal topological and geometrical relations. In contrast to symbolic reasoning with surrogate representation, diagrammatic reasoning uses direct representation. Therefore, combining surrogate and direct representations is a natural approach to diagrammatic reasoning. In this paper, we design and implement a hybrid reasoning system...
Data analysis for search logs is becoming more and more important and necessary. A search query may contain several keywords, which makes the text belong to different categories. This paper presents a new algorithm called Sequential Clustering Algorithm for clustering search logs. Different from many other clustering algorithms, the proposed algorithm can cluster one record into multiple categories...
Voting is an intriguing subject. Different voting schemes have different stabilities in retaining the original results in the presence of noise. A candidate could win an election under some voting schemes, even though he/she has less supporters than another candidate overall. This research introduces how sub-regional and regional voting systems change with the presence of concentrated noise. This...
This paper presents a non-traditional approach to detect outliers based on local information captured from neighbors. The Confidence-based Outlier Detection (COD) approach is proposed to explore the neighborhood for each target sample in order to obtain high detection confidence performed without being affected by irrelevant ambiguous data. In other words, the adopted SVM classifier is generated using...
In the last few years, ontologies have been successfully exploited by Decision Support Systems to represent knowledge and reason to take a decision. In this paper, an interval-gap-based logistics time ontology is modeled to represent the temporal knowledge in logistics for logistics decision support systems. The method to build time ontology for logistics is specified, and then linking properties...
Twice Indian blackouts occurred at the end of July in 2012 left over 600 million of people in the dark for several hours. In these two-day, Indian grid disturbances were regarded as the most serious and large-scale blackout in the world in history. A report has been generated by the enquiry committee which was organized by the Ministry of Power, Government of India, to investigate the factors which...
This paper proposes a general study of interval-valued fuzzy decision information systems by integrating fuzzy rough set theory with inclusion measure theory. By introducing a homogeneous inclusion measure between two interval-valued fuzzy sets, two knowledge acquisition approaches to information systems with interval-valued fuzzy decisions are proposed. Furthermore, the relationships between these...
This paper studies the cost benefit analysis (CBA) methods that can improve the decision support capability of smart grid deployment. Critical review based on various methodologies adopted worldwide has been carried out and detailed analysis was conducted. The finding demonstrates as there are no agreed guidelines for CBA. Also in real-life situation, due to large amount of data and data quality could...
The key to granular computing (GrC) is to make use of granules in problem solving. With the view point of GrC, the notion of a granule may be interpreted as one of the numerous small particles forming a larger unit. In many situations, there are different granules at different levels of scale in data sets having hierarchical scale structures. The multi-scale information system is a new and interesting...
Feature selection is an important topic in machine learning. In order to evaluate the candidate features, a strategy based on the constituent principle of the SVM optimal hyperplane is established in this paper. Then, by considering different feature combinations, a better feature subset can be obtained. The method is used to recognize the monomers in weather forecast, and experimental results demonstrate...
A novel vessel skeleton extraction method is presented in this work. Our work consists of three steps in a coarse-to-fine style: Firstly, by modeling the distance transform and its gradient vector field, the average outward flux of the gradient vector field is computed to coarsely label all image points. Then we introduce a topology-based shape thinning algorithm for extracting vessel skeleton tree...
In this paper, by the definitions of meet-irreducible element we discuss attribute characteristics and attribute reduction of formal contexts. We first propose an effective method to determine whether an element is meet-irreducible. Then present an approach to judge the indispensable attributes and the dispensable attribute, by which the attribute reduction approach of formal contexts is also obtained...
Along with emergence of the high dimensionality of data, feature selection techniques are getting more significant to learning algorithms. Many metrics have been introduced in feature selection. Among them, mutual information is a highlighted one and has been developed during the past years. In this paper, a novel feature selection method based on the measurement of complementarity of feature classification...
Semi-supervised dimensionality reduction is becoming one of the most popular fields nowadays. But the existing algorithms can not fully utilize the information in dimensionality reduction as the side information is treated equally. A new semi-supervised dimensionality reduction algorithm called Geodesic distance based semi-supervised locality dimensionality reduction (GSLDR) is proposed for the handwriting...
A new fuzzy clustering approach is presented based on two steps: data reduction and core data aggregation in a reduced subset of the original dataset. The data reduction largely reduces a number of data points in a dataset and simultaneously improves clustering quality based on a grid-based initialization for data space, where each grid is continuously bisected into two volume-equal smaller grids,...
This paper proposes a new approach of attribute reduction for decision systems based on rough set and fuzzy clustering in order to avoid information loss resulted from the discretization of real valued condition attributes. In this paper, the fuzzy clustering technique is employed to obtain an optimal value which measures the inconsistency between condition attributes and decision attribute, and attribute...
Fuzzy decision tree is useful for expressing fuzzy knowledge because of its readability. There have been several induction algorithms for fuzzy decision trees from real value data sets. In this paper, we propose a framework for generating fuzzy decision trees based on fuzzy rough techniques. Firstly, the ordinary fuzzification techniques are replaced by a clustering technique based on the tolerance...
In order to present a systematical method for selecting the parameters needed in the lower ad upper approximations of any approximated set about the probabilistic fuzzy compatibility approximation space, we present the decision-theoretic rough set over two universes based on fuzzy compatibility relation by using the Bayesian risk decision procedure over two universes.
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.