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In this paper, based on M-S model and level-set method, a new method for initializing level-set function and hierarchical constant segmentation is proposed in order to overcome the shortcomings in the Chan-Vese model. First, by improving the initial level set function, the process of re-initializing level set function in traditional method is eliminated, and the initial conditions are easier to handle...
Symmetrical wavelet and anti-symmetrical wavelet have characteristics of linear phase and generalized linear phase respectively. They are very important to signal decomposition and reconstruction, because they can avoid signal distortion. Daubechies wavelets and Symlets wavelets have being used widely. Unfortunately, they aren't symmetrical. In this paper, firstly, general symmetrical wavelet construction...
By analyzing the model for cardinal direction relations with objects' MBR (minimum boundary rectangle), the method of judging convex relations is put forward. The method and algorithm of consistency checking for atomic cardinal direction relations networks are proposed by combining path-consistency algorithm with the method of judging convex relations, as well as the algorithmic analysis and corresponding...
Clustering analysis is used to explore the classification for large dataset and Canberra distance is generalized so that it can process the data with categorical attributes. Based on the generalized Canberra distance definition, an instance of constraint-based clustering is introduced. Meanwhile, the nearest neighbor classification is improved. Class-labeled clusters are regarded as classifying models...
A method for encoding database is put forward in this paper. By this way, a record is denoted by only one binary number and so the size of the database is reduced sharply. If the database-encoding algorithm is used into some known modified algorithms, the efficiency will be improved remarkably. At the meantime, a new algorithm, anti-Apriori, which based on the proposed encoding method is introduced...
In this paper, we propose a new efficient data reduction algorithm through combining lattice with rough set. On the basis of lattice learning, the algorithm applies the concept of attribute reduction in the theory of rough sets and calculates the importance degree of attributes automatically by a density based approach. Under acceptable classification precision and complexity, it reduces row and column...
Recursive algorithm (RA) need a great number of storage space for continue operations (such as push) on stack, which easily causes stack overflow. In the paper, we present a novel recursive algorithm to solve this intractability throw constructing recursive tree, which only restoring valuable data for decreasing the spatial complexity, and then traversing recursive tree. In this way, the problem of...
This paper investigates an improved fuzzy multicategory support vector machines classifier (IFMSVM). It uses knowledge of the ambiguity associated with the membership of data samples of a given class and relative location to the origin, to improve classification performance with high generalization capability. In some aspects, classifying accuracy of the new algorithm is better than that of the classical...
A fast algorithm named FAST_LCS is presented for the longest common substring (LCS) problem. The algorithm firstly seeks the successors of the initial identical character tuples according to successor tables to obtain all the identical tuples and their levels. Then the result of LCS can be obtained by tracing back from the identical character tuple with the largest level. For n biosequences X 1, X...
A novel artificial immune network (AINet) algorithm is proposed in this paper. In the algorithm, a new method is introduced to confirm searching radius, which is based on "the age"-the generations when the cell exists in the network. Moreover, a novel preserving method is proposed to avoid the instability and degradation of the optimal results. These two measures not only increase the local...
The transportation problem (TP) is well known as a basic network problem for it could be extensively applied in many fields. The linear transportation problem (LTP), which is the core and basic model of TP, can be extended to other TP with higher complexity. In the present paper, a new particle swarm optimization algorithm (PSO-TP) whose special structure and operators are different from the classical...
We present the concept of the complexity radii of nonlinear dynamic system (NDS) with linear perturbations. In this paper we improve the algorithm of the complexity radii. As a "robust measure" of dynamic complexity of NDS, the complexity radii provide the tolerated parameter perturbation values of NDS without losing its dynamic complexity. As an application, the real complexity radii of...
This paper presents a mechanism called R_Apriori for learning rules from large datasets. The existing rough set based methods are not applicable for large data sets for its high time and space complexity. In this paper, large data sets are divided into several parts, in combination with Apriori algorithm, implicated rules are derived in liner relation to size of data set. At last, experiment result...
Outlier detection is one of the branches of data mining, with important applications in the domains of finance fraud detection, network intrusion analysis and so on. But most applications are high dimensional domains. Many algorithms use the concept of proximity to find outliers based on the relationship to the data set. However, the sparsity of high dimensional points results to the algorithms are...
To induce rules from numerical data by rough sets, there are two kinds of methods. One is to discretize the original data and then apply the crisp rough sets models. Here the rough sets models which can only deal with the nominal data are called crisp rough sets models. The other is to fuzzify the original data and then apply fuzzy rough sets models. There are some problems on both of these methods...
In this paper, we set up sticker DNA chip model whose essential bio-chemical experiments and their realization of process are discussed. Then we put forward a DNA algorithm of making spanning tree problem and its bio-chemical implement process. The algorithm is first creating an original solution set of spanning subgraph then filtering the set to obtain all spanning trees. And then we prove the validity...
A new kind of clustering algorithm called LOCAHID is presented in this paper. LOCAHID views each potential cluster as a tight coupling structure, which can be described by a density tree. Every density tree is dynamically generated according to its local density distribution. Those "closer" clusters are merged if some conditions are satisfied. In order to extend its applications to large...
In containing order rough set methodology (CORS), ordered attribute `criterion' is introduced. Some terminologies on rules or rules set, such as robust, minimality, completeness, mutuality degree, and conflict are discussed. The rules generation algorithm IGRs is given and the details of algorithm IGRs are studied. Heuristic knowledge, which is mutuality degree of a condition item with a decision...
Model selection is important in deciding among competing computational models in many scientific research domains including in cognition processing. This paper presents an information geometric model selection criterion GMSC and shows its application in cognition. IGMSC computes the geometric complexity of the model by regarding the model space as the manifold and estimates the model-data geometric...
Beam search algorithm, as an adaptation of branch and bound method, is regarded as one of the effective approaches in solving combinational optimization problems. In this paper, a new beam search algorithm for the large-scale permutation flow shop problem is proposed. A new branching scheme is addressed and compared with the traditional branching scheme. With the new branching scheme, the number of...
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