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In order to discriminate normal and abnormal heart sounds (HSs) accurately and effectively, a new method for clinical diagnosis of the heart valve diseases is proposed. The method is composed of three stages. The first stage is the preprocessing stage. During the pre-processing stage, the improved wavelet threshold shrinkage denoising algorithm is used for the noise reduction of the measured HSs....
This paper presents a method of automatic acquisition threshold value that changes dynamically with data distribution by polynomial fitting technique. The proposed method overcomes the sensitivity to cluster centers and locality of FCM (fuzzy c-means) algorithm and establishes an index mechanism on the basis of above analysis. Simulations show that the adaptable fuzzy clustering image indexing performs...
This paper presents the text clustering algorithm of the cluster intelligent optimization that uses cluster intelligent optimization algorithms to find the initial focal point to resolve the input sequences of fuzzy clustering and the initial point of sensitive issues; and use the improved fuzzy clustering algorithm to eliminate the sample text is not balance and refine clustering results. The experimental...
Currently, a large number of clustering algorithms are available for data mining. But it will be difficult for people who to a large extent know little about data mining to select an appropriate clustering algorithm. In order to solve this problem, in this paper, we first comprehensively analyze a number of clustering algorithms, then summarize their evaluation criteria and apply the so-called fuzzy...
The grid index is an important class of indexing technique in the spatial database. Grid index is widely used in the K - nearest neighbor algorithm, the algorithm proposed in this paper is based on the grid index, find the data objects intersecting with the given circle area or contained in the given circle area and cluster the grids which these data objects are in, the remaining grids will be used...
Many ellipse detection algorithms produce multiple elliptic hypotheses corresponding to a single elliptic object. Thus, it is needed to identify similar ellipses that possibly belong to the same object and cluster them as a single object. This will reduce the computational and memory requirement for further higher level processing of ellipse detection algorithms. Here, we present a method better than...
Video segmentation is a crucial pass to content-based video summarization and retrieval. In this paper, we present a practical method to efficiently group video content into semantic segments. First we detect shots with double-threshold method to find raw shots quickly, followed by redundant frames removal though spatial color distribution to get the key frames. Finally, we cluster the key frames...
Intrusion Detection Systems (IDSs) are known to produce huge volumes of alerts. The interesting alerts are always mixed with irrelevant, duplicate and non interesting alerts. Huge volumes of poorly sorted and unclustered alerts frustrate the efforts of analysts when identifying the interesting alerts. Therefore, the unmanageable amount of poorly sorted alerts is a critical issue affecting the performance...
Topic model is an increasing useful tool to analyze the semantic level meanings and capture the topical features. However, there is few research about the comparative study of the topic models. In this paper, we describe our comparative study of three topic models in the extrinsic application of topic clustering. The topic model distance is defined on the converged parameters of topic models, which...
In the paper, we formulate a new energy function followed by the use of graph cuts to refine the disparity map which takes segment as node. Firstly, the robust disparity plane fitting is modeled and the method of Singular Value Decomposition (SVD) is used to solve least square. In order to ensure reliable pixel sets for the segment, we filter out outliers through three main rules, namely; cross-checking,...
K-Means algorithm is an unsupervised clustering algorithm that classifies the input data points into multiple classes based on their inherent distance from each other. Success of k-means color image segmentation depends on parameter k. If numbers of clusters are estimated correctly, k-means image segmentation can provide good results. This paper proposes a novel method based on edge detection to estimate...
We propose an automatic moment-based image recognition technique in this paper. The problem to be solved consists of classifying the images from a set, using the content similarity. In the feature extraction stage, we compute a set of feature vectors using area moments. An automatic unsupervised feature vector classification approach is proposed next. It uses a hierarchical agglomerative clustering...
In order to improve the efficiency of regression testing, many test selection techniques have been proposed to extract a small subset from a huge test suite, which can approximate the fault detection capability of the original test suite for the modified code. This paper presents a new regression test selection technique by clustering the execution profiles of modification-traversing test cases. Cluster...
For the robot vision system in apple harvesting robot, a new image segmentation method based on entropy clustering is proposed in HSI color space. Firstly, noise was wiped off by using weighted algorithm of median filtering in HSI color space instead of traditional algorithm in RGB model; secondly, Hue and Saturation components were extracted to do entropy clustering with their independence with Intensity,...
Enlightened by the properties of scale-free network model, a novel particle swarm optimization with highly-clustered scale-free network model (HCSN-PSO) is proposed combining preferential attachment for degree and close nodes. Experimental simulations show that the new method obtains better convergence performance.
Detecting a high-quality moving object with good robustness in computer vision system has important significance for follow-up task. Researching on the traditional algorithm, this paper proposes a background reconstruction algorithm based on a modified k-means clustering and the Single Gaussian model which could provide an accurate background image through a sequence of scene images with foreground...
Similarity is an important concept in information theory. A challenging question is how to measure the amount of shared information between two systems. A large number of metrics are proposed and used to measure similarity between two computer programs or two portions of the same program. In this paper, we present an approach for assessing which metrics are most useful for similarity prediction in...
A model of database anomalous detection is designed in this paper. The model can not only describe the users' behavioral profile more accurately, but also improve the accuracy of database anomalous detection. Based on the designed model, Apriori-kl algorithm, which combines the K-means clustering algorithm with the improved Apriori algorithm, is presented to mine users' behavior profile preferably...
In the paper, we propose a new d-hop Clustering method for a clustering-based multi-hop routing scheme in large-scale wireless sensor network. d-hop clustering means that each cluster contains all nodes that are at distance at most d-hops from the clusterhead, so that the number of clusters can be getting smaller to make it possible to guarantee the combined system performances including end-to-end...
Cluster analysis becoming increasingly essential in data mining field, and is mainly used to discover the valuable data distribution and data mode in the potential datum. Based on the pheromone studies on basic clustering model, the theory of information entropy and two classical clustering analysis algorithms, an algorithm of K-means based on the pheromone is presented firstly. The algorithm works...
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