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1.
In this article the searching capability of genetic algorithms has been exploited for automatically evolving the number of clusters as well as proper clustering of any data set. A new string representation, comprising both real numbers and the do not care symbol, is used in order to encode a variable number of clusters. The Davies-Bouldin index is used as a measure of the validity of the clusters. Effectiveness of the genetic clustering scheme is demonstrated for both artificial and real-life data sets. Utility of the genetic clustering technique is also demonstrated for a satellite image of a part of the city Calcutta. The proposed technique is able to distinguish some characteristic landcover types in the image.  相似文献   

2.
An important approach for image classification is the clustering of pixels in the spectral domain. Fast detection of different land cover regions or clusters of arbitrarily varying shapes and sizes in satellite images presents a challenging task. In this article, an efficient scalable parallel clustering technique of multi-spectral remote sensing imagery using a recently developed point symmetry-based distance norm is proposed. The proposed distributed computing time efficient point symmetry based K-Means technique is able to correctly identify presence of overlapping clusters of any arbitrary shape and size, whether they are intra-symmetrical or inter-symmetrical in nature. A Kd-tree based approximate nearest neighbor searching technique is used as a speedup strategy for computing the point symmetry based distance. Superiority of this new parallel implementation with the novel two-phase speedup strategy over existing parallel K-Means clustering algorithm, is demonstrated both quantitatively and in computing time, on two SPOT and Indian Remote Sensing satellite images, as even K-Means algorithm fails to detect the symmetry in clusters. Different land cover regions, classified by the algorithms for both images, are also compared with the available ground truth information. The statistical analysis is also performed to establish its significance to classify both satellite images and numeric remote sensing data sets, described in terms of feature vectors.  相似文献   

3.
The objective of brain image segmentation is to partition the brain images into different non-overlapping homogeneous regions representing the different anatomical structures. Magnetic resonance brain image segmentation has large number of applications in diagnosis of neurological disorders like Alzheimer diseases, Parkinson related syndrome etc. But automatically segmenting the MR brain image is not an easy task. To solve this problem, several unsupervised and supervised based classification techniques have been developed in the literature. But supervised classification techniques are more time consuming and cost-sensitive due to the requirement of sufficient labeled data. In contrast, unsupervised classification techniques work without using any prior information but it suffers from the local trap problems. So, to overcome the problems associated with unsupervised and supervised classification techniques, we have proposed a new semi-supervised clustering technique using the concepts of multiobjective optimization and applied this technique for automatic segmentation of MR brain images in the intensity space. Multiple centers are used to encode a cluster in the form of a string. The proposed clustering technique utilizes intensity values of the brain pixels as the features. Additionally it also assumes that the actual class label information of 10% points of a particular image data set is also known. Three cluster validity indices are utilized as the objective functions, which are simultaneously optimized using AMOSA, a modern multiobjective optimization technique based on the concepts of simulated annealing. First two cluster validity indices are symmetry distance based Sym-index and Euclidean distance based I-index, which are based on unsupervised properties. Last one is a supervised information based cluster validity index, Minkowski Index. The effectiveness of this proposed semi-supervised clustering technique is demonstrated on several simulated MR normal brain images and MR brain images having some multiple sclerosis lesions. The performance of the proposed semi-supervised clustering technique is compared with some other popular image segmentation techniques like Fuzzy C-means, Expectation Maximization and some recent image clustering techniques like multi-objective based MCMOClust technique, and Fuzzy-VGAPS clustering techniques.  相似文献   

4.
Classifying the pixels of satellite images into homogeneous regions is a very challenging task as different regions have different types of land covers. Some land covers contain more regions, while some contain relatively smaller regions (e.g., bridges, roads). In satellite image segmentation, no prior information is available about the number of clusters. Here, in this paper, we have solved this problem using the concepts of semi-supervised clustering which utilizes the property of unsupervised and supervised classification. Three cluster validity indices are utilized, which are simultaneously optimized using AMOSA, a modern multiobjective optimization technique based on the concepts of simulated annealing. The first two cluster validity indices, symmetry distance based Sym-index, and Euclidean distance based I-index, are based on unsupervised properties. The last one is a supervised information based cluster validity index, Minkowski index. For supervised information, initially fuzzy C-mean clustering technique is used. Thereafter, based on the highest membership values of the data points to their respective clusters, randomly 10 % data points with their class labels are chosen. The effectiveness of this proposed semi-supervised clustering technique is demonstrated on three satellite image data sets of different cities of India. Results are also compared with existing clustering techniques.  相似文献   

5.
Recent advancement in microarray technology permits monitoring of the expression levels of a large set of genes across a number of time points simultaneously. For extracting knowledge from such huge volume of microarray gene expression data, computational analysis is required. Clustering is one of the important data mining tools for analyzing such microarray data to group similar genes into clusters. Researchers have proposed a number of clustering algorithms in this purpose. In this article, an attempt has been made in order to improve the performance of fuzzy clustering by combining it with support vector machine (SVM) classifier. A recently proposed real-coded variable string length genetic algorithm based clustering technique and an iterated version of fuzzy C-means clustering have been utilized in this purpose. The performance of the proposed clustering scheme has been compared with that of some well-known existing clustering algorithms and their SVM boosted versions for one simulated and six real life gene expression data sets. Statistical significance test based on analysis of variance (ANOVA) followed by posteriori Tukey-Kramer multiple comparison test has been conducted to establish the statistical significance of the superior performance of the proposed clustering scheme. Moreover biological significance of the clustering solutions have been established.  相似文献   

6.
Microarray technology has made it possible to monitor the expression levels of many genes simultaneously across a number of experimental conditions. Fuzzy clustering is an important tool for analyzing microarray gene expression data. In this article, a real-coded Simulated Annealing (VSA) based fuzzy clustering method with variable length configuration is developed and combined with popular Artificial Neural Network (ANN) based classifier. The idea is to refine the clustering produced by VSA using ANN classifier to obtain improved clustering performance. The proposed technique is used to cluster three publicly available real life microarray data sets. The superior performance of the proposed technique has been demonstrated by comparing with some widely used existing clustering algorithms. Also statistical significance test has been conducted to establish the statistical significance of the superior performance of the proposed clustering algorithm. Finally biological relevance of the clustering solutions are established.  相似文献   

7.
This article proposes an evolutionary-fuzzy clustering algorithm for automatically grouping the pixels of an image into different homogeneous regions. The algorithm does not require a prior knowledge of the number of clusters. The fuzzy clustering task in the intensity space of an image is formulated as an optimization problem. An improved variant of the differential evolution (DE) algorithm has been used to determine the number of naturally occurring clusters in the image as well as to refine the cluster centers. We report extensive performance comparison among the new method, a recently developed genetic-fuzzy clustering technique and the classical fuzzy c-means algorithm over a test suite comprising ordinary grayscale images and remote sensing satellite images. Such comparisons reveal, in a statistically meaningful way, the superiority of the proposed technique in terms of speed, accuracy and robustness.  相似文献   

8.
In the last decade, outlier detection for temporal data has received much attention from data mining and machine learning communities. While other works have addressed this problem by two-way approaches (similarity and clustering), we propose in this paper an embedded technique dealing with both methods simultaneously. We reformulate the task of outlier detection as a weighted clustering problem based on entropy and dynamic time warping for time series. The outliers are then detected by an optimization problem of a new proposed cost function adapted to this kind of data. Finally, we provide some experimental results for validating our proposal and comparing it with other methods of detection.  相似文献   

9.
In this paper we propose a new approach for land cover classification of remote sensing imagery. It is a two-stage technique, where in the first stage the global feature-based technique of histogram thresholding generalized to multidimensional cases developed by Khotanzad and Bouarfa (1990) is used, and in the second stage a local feature-based region growing technique is generalized to grow multiple non-contiguous regions in parallel. The Khotanzad and Bouarfa technique has the advantage of being a non-iterative unsupervised classification technique, but suffers from a failure to detect regions of small spatial extent which may have high local contrast but low weightage in the global feature space. Our proposed technique divides the image into blocks of suitable size so that regions of small spatial extent are detected in the block's histogram, and they are grown across neighboring blocks. The proposed technique is illustrated with actual remote sensing imagery. A number of choices of feature space for the first stage, and different measures of similarity for the second stage were investigated on remote sensing data, both visually as well as quantitatively in terms of classification accuracy. It was found that the xyz colour space (Ohta et al. 1980) for the first stage, and the J-M distance for the second stage similarity measure, gave the best results in terms of classification accuracy. Though the approach is unsupervised and non-iterative in nature, it has given a classification accuracy of better than 91 per cent for a five-class landcover classification.  相似文献   

10.
Microarray technology has been widely applied in study of measuring gene expression levels for thousands of genes simultaneously. In this technology, gene cluster analysis is useful for discovering the function of gene because co-expressed genes are likely to share the same biological function. Many clustering algorithms have been used in the field of gene clustering. This paper proposes a new scheme for clustering gene expression datasets based on a modified version of Quantum-behaved Particle Swarm Optimization (QPSO) algorithm, known as the Multi-Elitist QPSO (MEQPSO) model. The proposed clustering method also employs a one-step K-means operator to effectively accelerate the convergence speed of the algorithm. The MEQPSO algorithm is tested and compared with some other recently proposed PSO and QPSO variants on a suite of benchmark functions. Based on the computer simulations, some empirical guidelines have been provided for selecting the suitable parameters of MEQPSO clustering. The performance of MEQPSO clustering algorithm has been extensively compared with several optimization-based algorithms and classical clustering algorithms over several artificial and real gene expression datasets. Our results indicate that MEQPSO clustering algorithm is a promising technique and can be widely used for gene clustering.  相似文献   

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