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Texture segmentation by Pseudo Jacobi -Fourier moments is presented in this paper. Given a window size, moments for each pixel in the image are computed within small local windows, and then texture feature images be obtained by using a nonlinear transducer. Finally, each pixel in the image is classified by K-mean clustering algorithm.
An attempt has been made in the paper to find globally optimal cluster centers for remote-sensed images with the proposed Rapid Genetic k-Means algorithm. The idea is to avoid the expensive crossover or fitness to produce valid clusters in pure GA and to improve the convergence time. The drawback of using pure GA in the problem is the usage of an expensive crossover or fitness to produce valid clusters...
A lot of on- site management works are involved in the process of cluster project management, especially the treatment of sudden accidents which would produce plenty of knowledge and experience. As cluster project on-site management spots change frequently, knowledge produced by mobile workers in the process of on-site management is hard to be saved into the knowledge repository on time. It may cause...
We consider fc-median clustering in finite metric spaces and fc-means clustering in Euclidean spaces, in the setting where k is part of the input (not a constant). For the fc-means problem, Ostrovsky et al. show that if the optimal (k - 1)-means clustering of the input is more expensive than the optimal fc-means clustering by a factor of 1/∈2, then one can achieve a (1 + f(∈))-approximation to the...
The increasing availability of huge amounts of data pertaining to time and position of moving objects generated by different sources using a wide variety of technologies (e.g., RFID tags, GPS, GSM networks) leads to large spatial data collections. Mining such amounts of data is challenging, since the possibility to extract useful information from this peculiar kind of data is crucial in many application...
Advantages of None Euclidean Relational Fuzzy C-means (NERFCM) is analysed, by which four Fuzzy C-means (FCM) clustering algorithms are compared, which includes Fuzzy C-means (FCM) and traditional Relational Fuzzy C-means (RFCM) and None Euclidean Relational Fuzzy C-means (NERFCM) and Any Relational Fuzzy C-means (ARFCM). Their common points and different limitations on usage are discussed, finally...
According to the traditional morphological classification divide the quality of traditional Chinese medicine White Peony Root into first grade second grade and the third grade. Discrete the chromatography data of the White Peony Root which obtained under the condition of standard test and also make the information reduction. Obtaining the great peaks of linear independent vectors and obtaining every...
Mobile ad hoc networks are becoming an important concept of modern communication technologies and services. It provides some advantages to this communication world such as self-organizing and decentralization. In this paper, we are going to design a cluster-based multi source multicast routing protocol with new cluster head election, path construction and maintenance techniques. The main objective...
The following topics are dealt with: linear approximation; license plate recognition; color image segmentation; image quantization; wireless video transmission; congestion control; stochastic search; transmembrane helical segments; wavelet transform; semisupervised cluster algorithm; anomaly detection; data privacy; online market information processing; user behavior; particle swarm optimization;...
Building the quantum clustering model by quantum characteristic. It is proved by the Simulation experiment, that It can deal with exceptional, high-dimension complicated data and large-scale data set.
There has been much progress on efficient algorithms for clustering data points generated by a mixture of k probability distributions under the assumption that the means of the distributions are well-separated, i.e., the distance between the means of any two distributions is at least Ω(k) standard deviations. These results generally make heavy use of the generative model and particular properties...
In this paper we propose is an extension of kernel k-means clustering algorithm for symbolic interval data with aggregated kernel functions. To evaluate this method, experiments with synthetic interval data set was performed and we have been compared our method with a dynamic clustering algorithm with single adaptive distance. The evaluation is based on an external cluster validity index (corrected...
K-Means algorithm is one of the most used clustering algorithm for Knowledge Discovery in Data Mining. Seed based K-Means is the integration of a small set of labeled data (called seeds) to the K-Means algorithm to improve its performances and overcome its sensitivity to initial centers. These centers are, most of the time, generated at random or they are assumed to be available for each cluster....
Most current anomaly detection systems employ supervised methods or unsupervised methods. Supervised methods rely on labeled training data, however, in practice, this training data is typically expensive to produce. In contrast, unsupervised method can work without the need for massive sets of pre-labeled training data, but its accuracy is quite low. This paper put forward a semi-supervised cluster...
Edited video recordings, such as talk-shows and sitcoms, often include Audio-Visual clusters: frequent repetitions of closely related acoustic and visual content. For example during a political debate, every time that a given participant holds the conversational floor, her/his voice tends to co-occur with camera views (i.e. shots) showing her/his portrait. Differently from the previous Audio-Visual...
Document clustering organizes documents into groups such that each group contains documents with similar content. This paper presents the results of an experimental study of some common document clustering techniques. In particular, comparison of Euclidean K-means (K-Means), Spherical K-means(SK-Means) and unsupervised Principal Direction Divisive Partitioning (PDDP) algorithms is done. A comparative...
The paper presents a novel multi-factorial approach for robust real-time object tracking. The target object is modeled using joint features of color (Intensity) histogram bins, texture, shape. In subsequent frames of a video, target localization is done by generating a confidence-map (a binary image) which discriminates foreground and background using K-means clustering algorithm. Random samples (sample...
The selection of the clustering parameter based on k-means plays an important part in the cell image segmentation. By combination different clustered image's color information entropy calculation with original image, it can gain the optimal clustering number for color cell image segmentation. It also introduces a clustering number k-related mutual information and mutual information error calculation...
Node clustering has wide-ranging applications in decentralized P2P networks such as P2P file sharing systems, mobile ad-hoc networks, P2P sensor networks, and so forth. This paper proposes an approach to construct clusters in unstructured P2P networks based on small-world theory. In contrast to centralized graph clustering algorithms, our scheme is completely decentralized and it only uses the knowledge...
Intrusion detection systems (IDS) usually trigger a great number of alarm messages that frequently overwhelm their human operators. Hierarchically clustering technique is able to help IDS operators to get meaningful overviews from the great number of alarms. A dilemma is encountered when the clusters are generated. If the clusters are obtained one by one, they cannot be prevented from overlapping...
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