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.
In this paper, a novel approach to MRI Brain Image segmentation based on the Hybrid Parallel Ant Colony Optimization (HPACO) with Fuzzy C-Means (FCM) Algorithm have been used to find out the optimum label that minimizes the Maximizing a Posterior (MAP) estimate to segment the image. There are M colonies, M-1 colonies treated as slaves and one colony for master. Each colonies visit all the pixels with...
In this paper, we investigate the maximization of the amount of gathered data in a clustered wireless sensor network (WSN). The amount of gathered data is maximized by (1) choosing the optimal transmit power, and (2) selecting the optimal cluster head. For problem (1), we find closed-form solutions for the optimal or near optimal transmit power of cluster members (CM). For problem (2), we propose...
In CBR system, the case base is becoming increasingly larger with the incremental learning which results in the decline of case retrieval efficiency and its weaker performance. Aiming at such weakness of CBR system, this article proposes a novel case retrieval method based on Hybrid Ant-Fish Clustering Algorithm (HA-FC). At beginning of algorithm, we get rough cluster sets utilizing the advantage...
The existence of fake tea from non-origin impacts on the credibility and sales of the origin Longjing tea seriously. In order to weaken this impact, we proposed a technology using ant colony clustering algorithm in discrimination the origin of Longjing tea. Then acquired and analyzed the characteristics of the origin tea comprehensively, the 16 parameters of the images and spectra from each sample...
An alternative way for increasing the efficiency of transportation management system is to usef dynamic multi-zone dispatching. This problem concentrates on the quantities of inbound and outbound in each area and it is modified from the multi-zone dispatching. The factors of the rearrangement penalty of the area, in each zone, including time periods are also included. The objective of this research...
In the recent years, forests of decision trees have seen an increasing interest from the Machine Learning community since they allow to aggregate the decisions from a set of decision trees into one robust answer. However, this approach suffers from two well-known limits: first, their performances depend on the number of trees and thus finding the right size and how to aggregate decisions could be...
In order to overcome the easily-occurred precocious defects in solving complex combinatorial optimization problems with the basic ant colony algorithm, an improved ant colony algorithm based on information entropy is studied, using the path selection controlled by information entropy and random perturbations strategy to realize adaptive regulation of the algorithm in this paper.
Graphs are increasingly used to model a variety of loosely structured data such as biological or social networks and entity-relationships. Given this profusion of large-scale graph data, efficiently discovering interesting substructures buried within is essential. These substructures are typically used in determining subsequent actions, such as conducting visual analytics by humans or designing expensive...
Today's applications deal with multiple types of information: graph data to represent the relations between objects and attribute data to characterize single objects. Analyzing both data sources simultaneously can increase the quality of mining methods. Recently, combined clustering approaches were introduced, which detect densely connected node sets within one large graph that also show high similarity...
Projective Clustering Ensembles (PCE) has recently been formulated to solve the problem of deriving a robust projective consensus clustering from an ensemble of projective clustering solutions. PCE is formalized as an optimization problem with either a two-objective or a single-objective function, depending on whether the object-based and the feature-based representations of the clusters in the ensemble...
Kernel-based clustering is one of the most popular methods for partitioning nonlinearly separable dataset. However, exhaustive search for the global optimum is NP-hard. Iterative procedure such as k-means can be used to seek one of the local minima. Unfortunately, it is easily trapped into degenerate local minima when the prototypes of clusters are ill-initialized. In this paper, we restate the optimization...
Most well-known discriminative clustering models, such as spectral clustering (SC) and maximum margin clustering (MMC), are non-Bayesian. Moreover, they merely considered to embed domain-dependent prior knowledge into data-specific kernels, while other forms of prior knowledge were seldom considered in these models. In this paper, we propose a Bayesian maximum margin clustering model (BMMC) based...
The modularity function is a widely used measure for the quality of a graph clustering. Finding a clustering with maximal modularity is NP-hard. Thus, only heuristic algorithms are capable of processing large datasets. Extensive literature on such heuristics has been published in the recent years. We present a fast randomized greedy algorithm which uses solely local information on gradients of the...
Ant colony optimisation has traditionally been used to solve problems that have few/light constraints or no constraints at all. Algorithms to maintain and restore feasibility have been successfully applied to such problems. Set partitioning is a very constrained combinatorial optimisation problem, for which even feasible solutions are difficult to construct. In this paper a binary ant colony optimisation...
Web based Decision Support systems like recommendation systems have become effective tools for decision making in the recent past. However the recommender systems employing conventional clustering techniques (KRS) like K-Means for collaborative filtering, suffer from the limitation of getting local optimum results. This paper presents Memetic Recommender System (MRS) based on the collaborative behavior...
The advantages of soft c-means over its hard and fuzzy versions render it more attractive to use in a wide variety of applications. Its main merit lies in its relatively higher convergence speed, which is more obvious in the presence of huge high dimensional data. This work presents a new approach to accelerate the convergence of the original soft c-means. It is mainly based on an iterative optimization...
The necessity of lowering the execution of system tests' cost is a consensual point in the software development community. The present study presents an optimization of the regression tests' activity, by adapting a test cases prioritization technique called Failure Pursuit Sampling-previously used and validated for the prioritization of tests in general-improving its efficiency for the exclusive execution...
It is introduced of an image analysis technique for the adaptive measuring of the gap width between cylinder cover and socket sleeve. The relative contents include the overall structure presentation of the measuring device, the differential analysis of the gap image, the first ant colony algorithm for the clustering number unknown, the second ant colony algorithm for the clustering number known, and...
For the limited application and shortcoming of FCM (Fuzzy C-Means) clustering algorithm, an improved automatic FCM clustering algorithm is put forward. First, the fuzzy equivalent matrix is achieved by fuzzier the standard uniform data sets; then, the objective function of the improved automatic FCM clustering algorithm is optimized by the amendment of membership function and distance measuring function;...
Task scheduling is an important part of high performance multi-core building. The shortcomings of existing task scheduling algorithms is analyzed, and a new efficient heuristic task scheduling algorithm, namely,HCDDSL is proposed in this paper. Firstly, the new algorithm optimizes DAG graph by using clustering, then the nodes are descended by the values of Succ_sum,the task schedule has been processed...
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.