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We propose a new approach to tackle the well known fuzzy c-means (FCM) initialization problem. Our approach uses a metaheuristic search method called Harmony Search (HS) algorithm to produce near-optimal initial cluster centers for the FCM algorithm. We then demonstrate the effectiveness of our approach in a MRI segmentation problem. In order to dramatically reduce the computation time to find near-optimal...
The inherently hierarchical problem of evaluating the complexity of an image interpretation is of relevance in both computer science and cognitive psychology. In this paper a new method of rule generation for the hierarchical prioritized fuzzy system, HPFS, is proposed, which overcomes the problem of lack of interpretability of most of the traditional fuzzy systems in modelling image. A hierarchical...
This paper shows text document dimension reduction and clustering technique which is called the bigradient learning algorithm. This algorithm is based on the two learning parameters. The results show, that bigradient learning algorithm, used with proper selected values, does almost the same clustering as the other arbitrary PCA learning method by neural network. At the end, the three linear PCA methods...
The use of clustering as a data analysis tool has raised concerns about the violation of individual privacy. This paper proposes a data perturbation technique for privacy preservation in k-means clustering. Data objects that have been partitioned into clusters using k-means clustering are perturbed by performing geometric transformations on the clusters in such a way that the object membership of...
Over past few years, the studies of cultured neuronal networks have opened up avenues for understanding the ion channels, receptor molecules, and synaptic plasticity that may form the basis of learning and memory. The hippocampal neurons from rats are dissociated and cultured on a surface containing a grid of 64 electrodes. The signals from these 64 electrodes are acquired using a fast data acquisition...
Takagi-Sugeno models are an important class of fuzzy rule based oriented models, generally used for prediction and control. Fuzzy clustering is one of effective methods for identification. In this method, we propose to use a fuzzy clustering method (Kernel based fuzzy c-means method) for automatically constructing a multi-input fuzzy model to identify the structure of a fuzzy model. To clarify the...
Perceptual information is quickly gaining importance in mesh representation, analysis and rendering. User studies, eye tracking and other techniques are able to provide ever more useful insights for many user-centric systems, which form the bulk of computer graphics applications. In this work we build upon the concept of Mesh Saliency - an automatic measure of visual importance for triangle meshes...
In this paper, it is described a new unsupervised approach based on wavelet packet transform for texture images segmentation. This transform is able to decompose an image not only from the low frequency parts, but also from the middle-high frequency parts, in which there is a certain amount of texture information. After the extraction of the features, a clustering is carried out, by using an immune-inspired...
Several general-purpose algorithms and techniques have been developed for image segmentation. Since there is no general solution to the image segmentation problem, these techniques often have to be combined with domain knowledge in order to effectively solve an image segmentation problem for a problem domain. This paper presents a comparative study of the basic image segmentation techniques i.e. edge-based,...
Document clustering is an important tool for applications such as search engines and document browsers. It enables the user to have a good overall view of the information contained in the documents. The well-known methods of document clustering, however, do not really address the special problems of text document clustering: very high dimensionality of the document, very large size of the datasets...
The size of publicly indexable World Wide Web has probably surpassed 14.3 billion documents and as yet growth shows no sign of leveling off. As more information becomes available on the Web it is more difficult to provide effective search services for Internet users. Since, it is assumed that users do not always formulate search queries using the best terms. So, search engines invoke query expansion...
Clustering analysis has been an emerging research issue in data mining due its variety of applications. In the recent years, it has become an essential tool for gene expression analysis. Many clustering algorithms have been proposed so far. However, each algorithm has its own merits and demerits and can not work for all real situations. In this paper, we present a clustering algorithm that is inspired...
Differential evolution has emerged as one of the fast, robust, and efficient global search heuristics of current interest. Besides its good convergence properties and suitability for parallelization, Differential evolution's main assets are its conceptual simplicity and ease of use. This paper describes an application of differential evolution to the fuzzy clustering for categorical data sets. The...
In the literature on subspace clustering, traditional clustering techniques have been extended for computing meaningful and interesting clusters in the appropriate subspaces of the high dimensional data. We present a novel algorithm to capture unobserved object relationships embedded in fuzzy subspaces. In order to model the uncertainties of fuzzy data, we propose a modification of fuzzy c-means algorithm...
In the recent years, major CPU designers have shifted from ramping up clock speeds to add on-chip multi-core processors. Algorithms and applications must be tuned to allow multi-core processors to exploit their inherent parallelism. An experiment is carried out with data mining (DM) algorithms, to explore the potential of quad-core hardware architecture with OpenMP API (application programming interface)...
In this paper we present a new clustering method based on K-means that have avoided alternative randomness of initial center. This paper focused on K-means algorithm to the initial value of the dependence of K selected from the aspects of the algorithm is improved. First, the initial clustering number is radicN. Second, through the application of the sub-merger strategy the categories were combined...
We begin this paper by describing our rationale and overall design of an exceptional client model based on data mining algorithm. Then, we continue by summarizing the simulation details and describing the type of results obtained from implementing the proposed system, which consists of three heterogeneous data mining algorithms. The idea behind the model is that three heterogeneous data mining algorithms...
Research and development activities (R&D) as the core competitiveness in the high-tech enterprises play an extremely important role and far-reaching significance. In order to measure the R&D performance of the high-tech enterprises scientifically and accurately, this paper introduces the tree-structured growing self-organizing maps (TGSOM) network into the spatial data mining (SDM) to be used...
Density-based clustering and density-based outlier detection have been extensively studied in the data mining. However, Existing works address density-based clustering or density-based outlier detection solely. But for many scenarios, it is more meaningful to unify density-based clustering and outlier detection when both the clustering and outlier detection results are needed simultaneously. In this...
Clustering is popular used in customer value segmentation in business research. Compared with other clustering methods, the objective clustering analysis can automatically and objectively determine the number of clusters and find out the optimal clustering scheme. This investigation discussed the reasonable evaluation system of value-driven customer segmentation, identified customer behavior using...
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