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This paper examines a schema for graph-theoretic clustering using node-based resilience measures. Node-based resilience measures optimize an objective based on a critical set of nodes whose removal causes some severity of disconnection in the network. Beyond presenting a general framework for the usage of node based resilience measures for variations of clustering problems, we emphasize the unique...
Clustering is applied to many applications and the decision with regards to which algorithm to use is dependent on the nature of the task to be carried out. Before choosing which clustering algorithm to use one needs to be aware of the nature of the task to be done and then determine the algorithm accordingly, based on the capabilities and performance metrics of that algorithm. This paper makes an...
We consider the problem of clustering noisy finite-length observations of stationary ergodic random processes according to their nonparametric generative models without prior knowledge of the model statistics and the number of generative models. Two algorithms, both using the L1-distance between estimated power spectral densities (PSDs) as a measure of dissimilarity, are analyzed. The first algorithm,...
Clustering is a technique in which a given data set is divided into groups called clusters in such a manner that the data points that are similar lie together in one cluster. Clustering plays an important role in the field of data mining due to the large amount of data sets. This paper reviews the various clustering algorithms available for data mining and provides a comparative analysis of the various...
In this paper, we propose a new consensus clustering algorithm, which is based on an existing clustering paradigm, called enhanced splitting merging awareness tactics (E-SMART). The problem of determining the number of clusters, which affects many state-of-theart consensus clustering algorithms, is addressed by the proposed CoCE-SMART algorithm. The idea behind CoCE-SMART is that SMART is used repeatedly...
Image segmentation has a positive impact in materials science, and it has application prospect and research value especially in the forecast of material performance. Considering spatial neighbourhood information can improve the accuracy of image segmentation, a novel modified FCM method for image segmentation is presented in this paper. This method take full advantage of the relevance of the current...
An important task in maritime search and inspection involves re-acquiring and identifying underwater objects by surveying the objects from multiple angles. Because of false contacts related to clutter on the sea floor, the objects are often detected in dramatically different densities in a given area. Previously developed methods to plan survey paths on groups of contacts led to efficient paths when...
Nowadays we communicate in a digital universe. In fact the amount of data (structured and unstructured) is exploding. That's what we call Big Data. The voluminous data are in the most of cases noisy and overlapping, their clustering makes critical challenges. In addition validating resulting partitions is a serious problem. In this paper we present a new fuzzy validity index able to interpret the...
Data mining is the process of extracting knowledge from the huge amount of data. The data can be stored in databases and information repositories. Data mining task can be divided into two models descriptive and predictive model. In Predictive model we can predict the values from different set of sample data, they are classified into three types such as classification, regression and time series. Descriptive...
This paper mainly introduces a practical algorithm called fuzzy-possibilistic c-means (FPCM) clustering algorithm. It is based on fuzzy c-means (FCM) clustering algorithm and possibilistic c-means (PCM) clustering algorithm. FPCM algorithm figures out the existing problems of the above two algorithms and produces both memberships and possibilities simultaneously. For example, FPCM algorithm works...
A clustering-based method to identify models that are piecewise affine or of Takagi-Sugeno type is presented. As prototype-based clustering algorithms, which are well suited for partitioning, frequently converge to unwanted local solutions, density-based noise clustering is used to initialize them. The clustering acts in a mixed parameter-position feature space and divides the data into separate sets...
Data Mining is all about data analysis techniques. It is useful for extracting hidden and interesting patterns from large datasets. Clustering techniques are important when it comes to extracting knowledge from large amount of spatial data collected from various applications including GIS, satellite images, X-ray crystallography, remote sensing and environmental assessment and planning etc. To extract...
Most of the clustering algorithms are affected by the number of attributes and instances with respect to the computation time. Thus, the data mining community has made efforts to enable induction of the clustering efficient. Hence, scalability is naturally a critical issue that the data mining community faces. A method to handle this issue is to use a subset of all instances. This paper suggests an...
Several clustering algorithms have been extensively used to analyze vast amounts of spatial data. One of these algorithms is the SNN (Shared Nearest Neighbor), a density-based algorithm, which has several advantages when analyzing this type of data due to its ability of identifying clusters of different shapes, sizes and densities, as well as the capability to deal with noise. Having into account...
Density-based clustering can detect arbitrary shape clusters, handle outliers and do not need the number of clusters in advance. However, they cannot work properly in multi density environments. The existing multi density clustering algorithms have some problems in order to be applicable for data streams such as the need of whole data to perform clustering, two-pass clustering and high execution time...
Clustering is an important tool which has seen an explosive growth in Machine Learning Algorithms. DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering algorithm is one of the most primary methods for clustering in data mining. DBSCAN has ability to find the clusters of variable sizes and shapes and it will also detect the noise. The two important parameters Epsilon (Eps)...
Clustering is one of the most valuable methods of computational intelligence field, in which sets of related objects are cataloged into clusters. Almost all of the well-known clustering algorithms require input number of clusters which is hard to determine but have a significant influence on the clustering result. Furthermore, the majority is not robust enough towards noisy data. In contrast, density...
Principal curves, as a nonlinear generalization of principal components, are a common tool used in multivariate analysis for ends like dimensionality reduction and feature extraction. However, one of the difficulties that arise when utilizing this technique is that efficiency of existing principal curves algorithms is often low when dealing with large data set owing to high computational complexity...
Fuzzy clustering is a popular method for image segmentation and various of models based on fuzzy clustering are proposed. However, many methods suffer from the slow convergence and sensitivity to noise and parameters. In this letter, a novel fuzzy clustering method for image segmentation is proposed to solve these problems. A kernel which incorporates the local spatial information is proposed to regularize...
Many real applications, such as network traffic monitoring, intrusion detection, satellite remote sensing, and electronic business, generate data in the form of a stream arriving continuously at high speed. Clustering is an important data analysis tool for knowledge discovery. Compared with traditional clustering algorithms, clustering stream data is an important and challenging problem which has...
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