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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...
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...
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...
We introduce a new method for discovering latent topics in sets of objects, such as documents. Our method, which we call PARIS (for Principal Atoms Recognition In Sets), aims to detect principal sets of elements, representing latent topics in the data, that tend to appear frequently together. These latent topics, which we refer to as `atoms', are used as the basis for clustering, classification, collaborative...
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;...
This paper proposes a hybrid framework in the study of ultrasound image segmentation that combines bottom-up (BU) with top-down (TD) strategy. The BU part with high precision constructs a weight matrix combining region- and edge- cues in terms of a novel idea of designing support regions, whereas the TD part incorporates location-driven prior knowledge from the doctor and spatial coherence information...
A novel algorithm which combines clustering analysis and SVM is proposed for classification. Specifically, based on the conglomeration and decentralization characteristics of the positive and negative samples, we present a new type of support vector machine called Clustered Grouping Support Vector Machine or GC-SVM. After clustering training, the samples are divided into different groups, then a series...
Based on the Ant Colony Algorithm with clustering processing, a new algorithm is proposed for solving the route optimization of surface mount. According to the actual condition, a new clustering technique for electronic component is provided to simplify the scale of the problem and to reduce the numbers of changing suction mouths; Integrated considering the features of informational hormones updating,...
Clustering may be named as the first clustering technique addressed by the research community since 1960s. However, as databases continue to grow in size, numerous research studies have been undertaken to develop more efficient clustering algorithms and to improve the performance of existing ones. This paper demonstrates a general optimization technique applicable to clustering algorithms with a need...
In the field of wireless sensor network, it is one of the hottest issues of current research that how to maintain the quality of network coverage and balance nodes' energy consumption to optimize the network lifetime. This paper analyzed LEACH (low energy adaptive clustering hierarchy) and proposed an energy-efficient distributed clustering algorithm on coverage (ECAC). In the algorithm, the redundancy...
Multiple description scalable coding based on T+2D wavelet decomposition structure is highly flexible for peer-to-peer (P2P) video streaming. Finding the optimal truncation point of each code block (CB) within each description is an NP-hard problem. To implement an efficient low-complexity solution, we propose a simple clustering algorithm for partitioning the CBs into a limited number of clusters,...
Ant-based techniques are designed to take biological inspirations on the behavior of these social insects. Data clustering techniques are classification algorithms that have a wide range of applications, from Biology to Image processing and Data presentation. Since real life ants do perform clustering and sorting of objects among their many activities, we expect that a study of ant colonies can provide...
It gives an Affinity Propagation Clustering algorithm embedded in Optimizable Dissimilarity Measure(APCODM), in order to find a more meaningful clustering distribution by searching a better dissimilarity measure in hybrid attributes data space. After some necessary discuss, it gives its time complexity and astringency analysis. The APCODM can some time get a better clustering quality validated by...
The fax clustering is that the same or similar faxes will be clustered into one group in mass faxes. In order to improve accuracy of clustering, a fax clustering algorithm based on adaptive ant colony optimization was proposed in this paper. The algorithm simulates ant feeding theory, and improves the coefficient of pheromone updating, and avoids falling into local optimum. The experimental results...
In this article, a new PMACO algorithm is proposed for capacitated P-Median Problem (CPMP). It devised a set of performing strategies of Ant Colony Algorithm (ACO) in view of the characteristic of CPMP. These strategies include the selecting strategy of initial medians, the pheromone learning strategy of object-assignment means and pheromone-smoothness strategy. They insure the PMACO algorithm can...
Image segmentation aims to partition an image into several disjointed regions that are homogeneous with regards to some measures so that subsequent higher level computer vision processing, such as object recognition, image understanding and scene description can be performed. Multi-objective formulations are realistic models for image segmentation because objectives under consideration conflict with...
This paper introduces an approach for enabling existing multi-view stereo methods to operate on extremely large unstructured photo collections. The main idea is to decompose the collection into a set of overlapping sets of photos that can be processed in parallel, and to merge the resulting reconstructions. This overlapping clustering problem is formulated as a constrained optimization and solved...
Fuzzy c-means clustering (FCM) algorithm is an important and basic tool of classification and analysis for no supervision data, which has been used extensively in pattern recognition, data analysis, image processing and fuzzy modeling. Although the FCM algorithm is an unsupervised machine self-learning algorithm, however, there are two parameters must be given appropriate assignment before conducting...
This paper presents a novel approach for solving the Multiple Sequence Alignment (MSA) problem. K-Means clustering is combined with the Rubber Band Technique (RBT) to introduce an iterative optimization algorithm, namely RBT-Km, to find the optimal alignment for a set of input protein sequences. In this technique, the MSA problem is modeled as a Rubber Band, while the solution space is modeled as...
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