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.
Recently, with wide use of computer systems, internet, and rapid growth of computer networks, the problem of intrusion detection in network security has become an important issue of concern. In this regard, various intrusion detection systems have been developed for using misuse detection and anomaly detection methodologies. These systems try to improve detection rates of variation in attack types...
Many research topics in recent years have focused on clustering algorithms as a scalable routing protocol in wireless sensor networks. In these algorithms, by the use of data aggregation techniques and reduction in sensors that should make long distance communications, energy consumption in total network lifetime is decreased. One of algorithms proposed in this field is DECSA. Aiming at the problem...
Energy proficiency is an essential feature to intend an extensive existence wireless sensor network. More than a few methods have been proposed to boost the battery lifetime of sensor nodes. One of the methods is clustering approach which consists of two ways: selecting cluster heads with more residual energy and rotating cluster heads periodically from cluster to cluster thus extends the network...
There are many factors affect the stability of reservoir slopes, each of them is associated and coupled with others. Generally, the analysis of slopes stability can be achieved by the method of effect-factors analogy and cluster analysis. Traditional cluster analysis is difficult to obtain the stable global optimal solution, since the results are sensitive to the initial cluster center and the order...
In 2010, we proposed the improved unsupervised possibilistic clustering algorithm (IUPC) that can be run as an unsupervised clustering and overcome the weakness of the unsupervised possibilistic clustering algorithm (UPC) that it tends to generate coincident clusters. IUPC inherits the merits of UPC. In the meanwhile, IUPC solves the coincident clusters problem of UPC by limiting the feasible regions...
The goal of image segmentation is to cluster pixels into salient image regions, it is the most significant step in image analysis. Thresholding is a simple but effective tool to separate objects from the background, which is one of the most popular algorithms. The artificial bee colony algorithm (ABC) is a recently presented meta-heuristic algorithm, which has been successfully applied to solve many...
The density at a data point is defined based on kernel function. And we introduce weight to refine rough k-means algorithm. Then we construct the formula for calculating local outlier score based on the clusters generated by the refined rough k-means algorithm. We use a synthetic data set and a real-world data set to verify that the new technique for local outliers detection is not only accurate but...
This paper proposes a new mesh simplification algorithm which makes effort in reducing the approximation error and improving the mesh regularity of simplified mesh at the same time. In previous mesh simplification researches, algorithms generally focused on the appearance error between the simplified mesh and the original mesh. However, a so-called high quality simplified mesh must have low approximation...
In 2008, we proposed a clustering algorithm called improved kernel based fuzzy c-means clustering algorithm (IKFCM) to improve the performance of the original fuzzy c-means clustering algorithm. In this paper, we analyze the convergence of the IKFCM by means of Zangwill's convergence theorem. The result shows that arbitrary sequences generated by IKFCM always terminates at a local minimum or saddle...
In embedded Linux applications, the power failure may lead to the abnormity of the FAT file system. An effective repairing algorithm for the FAT file system was designed and implemented in this paper. The scanning files method based on tree structure was utilized as the core of repairing algorithm. FAT tables and cluster markers were adopted to improve the memory efficiency. Furthermore, the abnormities...
In many telecom and web applications, there is a need to identify whether data objects in the same source or different sources represent the same entity in the real-world. This problem arises for subscribers in multiple services, customers in supply chain management, and users in social networks when there lacks a unique identifier across multiple data sources to represent a real-world entity. Entity...
Domain specific design of reconfigurable architecture is a hard and time-consuming job. In this paper, a fast and effective domain-specific design method is proposed which mainly concludes a top-down subgraph enumeration algorithm and a heuristic identification process based on topological searching. A clustering and splitting algorithm is used to enumerate all the maximal valid subgraphs (MVSs) of...
Cluster analysis is an important data mining technique used to find data segmentation and pattern information. By clustering the data, people can obtain the data distribution, observe the character of each cluster, and make further study on particular clusters. In addition, cluster analysis usually acts as the preprocessing of other data mining operations. Therefore, cluster analysis has become a...
One of the most critical issues in the wireless sensor networks is the network's nodes limited availability of energy. The network lifetime strictly depends on its energy efficiency. Clustering is a method to make the consumed energy efficient. LEACH is one of the fundamental clustering algorithms. In this paper we have proposed an algorithm which extends LEACH by considering number of neighbors and...
Data mining concerns theories, methodologies, and in particular, computer systems for knowledge extraction or mining from large amounts of data. Association rule mining is a general purpose rule discovery scheme. It has been widely used for discovering rules in medical applications. The diagnosis of diseases is a significant and tedious task in medicine. The detection of heart disease from various...
Fuzzy C-Means has been used as a popular fuzzy clustering method due to its simplicity and high speed in clustering large data sets. However, C-Means has two shortcomings: dependency on the initial state and convergence to local optima. In this paper a new algorithm based on simulated annealing and possibilistic noise rejection clustering is proposed to reduce the problem of converging to local minima...
Discovering clusters of varyingly shapes, sizes and densities in a data set is still a challenging problem for density-based algorithms. Recently presented approaches either require the input parameters involving the information about the structure of the data set, or are restricted to two-dimensional data. In this paper, we present a density-based clustering algorithm, which uses the fuzzy proximity...
The advent of DNA microarray technology has enabled biologists to monitor the expression levels (MRNA) of thousands of genes simultaneously. In this survey, we address various approaches to gene expression data analysis using clustering techniques. We discuss the performance of various existing clustering algorithms under each of these approaches. Proximity measure plays an important role in making...
Mining microarray data sets is important in bioinformatics research and biomedical applications. Recently, mining triclusters or 3D clusters in a Gene Sample Time or 3D microarray data is an emerging area of research. Each tricluster contains a subset of genes and a subset of samples such that the genes are coherent on the samples along the time series. There is a scarcity of triclustering algorithms...
A model of RBF neural network (RBFNN) is framed to solve the problem of identification of nonlinear system. In order to realize the structure identification of RBFNN, a kind of hybrid parameter optimization algorithm is proposed based on optimal selection cluster algorithm and PSO. By this algorithm, it is optimally gained the hidden layer node number of RBFNN in terms of input samples. Then the structure...
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.