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A possibilistic fuzzy c-means (PFCM)[1] has been proposed for clustering unlabeled data. It is a hybridization of possibilistic c-means (PCM) and fuzzy c-means (FCM), therefore it has been shown that PFCM is able to solve the noise sensitivity issue in FCM, and at the same time it helps to avoid coincident clusters problem in PCM with some numerical examples in low-dimensional data sets. In this paper,...
Video summarization is an efficient and flexible way to represent video data. In this paper, we use the kernel PCA and clustering based key frame extraction to realize multilevel video representation. In order to remove the redundancy caused by large scene changes, SIFT flow scene alignment is performed on the clustering set of key frames. After alignment, one representative frame is chosen from the...
In functional magnetic resonance imaging (fMRI) data, activated voxels are usually very small in number and are embedded in a mass of inactive voxels. For clustering analysis, this situation generates an ill-balanced data problem among different classes of voxels. In this paper we propose a novel method to overcome the ill-balanced data problem, by reducing the number of voxels to be processed by...
In large scale wireless sensor networks, clustering is an effective technique for the purpose of improving the utilization of limited energy and prolonging the network lifetime. However, the problem of unbalanced energy dissipation exists in the multi-hop clustering model, where the cluster heads closer to the sink have to relay heavier traffic and consume more energy than farther nodes. Unequal clustering...
Fuzzy clustering is an important problem which is the subject of active research in several real world applications. Fuzzy c-means (FCM) algorithm is one of the most popular fuzzy clustering techniques because it is efficient, straightforward, and easy to implement. However FCM is sensitive to initialization and is easily trapped in local optima. Particle swarm optimization (PSO) is a stochastic global...
Portfolio rebalancing problem is an extension of the basic portfolio optimization problem in which invested portfolio is rebalanced by incurring proportional transaction costs. The constraints included in this problem formulation are basic, cardinality, bounding and class constraints. Due to complex constraints, the solution to the problem has been beyond the reach of traditional methods. Hence, heuristic...
The K-Modes algorithm is one of the most popular clustering algorithms in dealing with categorical data. But the random selection of starting centers in this algorithm may lead to different clustering results and falling into local optima. In this paper we proposed a swarm-based K-Modes algorithm. The experimental results over two well known Soybean and Congressional voting categorical data sets show...
Content-based image retrieval (CBIR), also known as query by image content (QBIC) and content-based visual information retrieval (CBVIR) is the application of computer vision to the image retrieval problem, that is, the problem of searching for digital images in large databases. It is increasingly evident that an image retrieval system has to be domain specific. In this paper, we present an algorithm...
Due to the enormous size of the web and low precision of user queries, finding the right information from the web can be difficult if not impossible. One approach that tries to solve this problem is using clustering techniques for grouping similar documents together in order to facilitate presentation of results in more compact form and enable thematic browsing of the results set. Web search results...
In the k means clustering algorithm right value of clusters (k) are initially unknown and effective selections of initial seed are also difficult. In this paper efficient k-means algorithm is proposed and implemented which overcome initial seed problem and unknown number of cluster problem. The algorithm is applied on real BIST server log data and Gaussian dataset to test its accuracy and efficiency...
Detection of brain tumors from MRI is a time consuming and error-prone task. This is due to the diversity in shape, size and appearance of the tumors. In this paper, we propose a clustering algorithm based on Particle Swarm Optimization (PSO). The algorithm finds the centroids of number of clusters, where each cluster groups together brain tumor patterns, obtained from MR Images. The results obtained...
Entities of the real world require partition into groups based on even feature of each entity. Clusters are analyzed to make the groups homologous and well separated. Many algorithms have been developed to tackle clustering problems and are very much needed in our application area of gene expression profile analysis in bioinformatics. It is often difficult to group the data in the real world clearly...
This paper presents an incremental clustering algorithm based on DGC, a density-based algorithm we developed earlier. We experimented with real-life datasets and both methods perform satisfactorily. The methods have been compared with some well-known clustering algorithms and they perform well in terms of z-score cluster validity measure.
This paper intends to propose a novel clustering method based on ant colony (AC) algorithm. A new approach called TT-transform based time frequency analysis is used in processing the non-stationary power signal disturbances. The time-time transform is the inverse Fourier transform of S-transform. The proposed model is demonstrated using feature vector from the domain of power signal analysis, yielding...
The main objective of sensor deployment problem in Wireless Sensor Network (WSN) is to use minimum number of sensor nodes with given sensing range that can cover any target in the coverage area to monitor the environment. The optimal sensor deployment enables accurate sensing information on target behavior with minimum sensing range and number of sensor nodes. The target coverage terrain in a locality...
Protein structure prediction (PSP) is one of the most important problems in computational biology. And it also is a very difficult optimization task, especially for long sequence instances. This paper proposes a novel clustering based niching EDA for HP model folding problem. The EDA individuals are clustered by the affinity propagation clustering method before submitting them to niching clearing...
A lot of large distributed system can benefit from the implement of network coordinate system, which can estimate latencies among Internet hosts. In this paper, we focus on problems in building network coordinate system. Firstly, we analyze the disadvantages of some algorithms that based on fixed reference nodes and algorithms based on unfixed reference nodes. Then we propose a new architecture of...
Combining virtual machine technology and network computing technology will be able to effectively aggregate the widely distributed heterogeneous and autonomous resources in the Internet. This paper proposes a virtual machine server aggregation algorithm, called DVSA, based on hierarchical clustering method for virtual computing environment. According to network latencies, the algorithm clusters virtual...
This paper presents the application of fuzzy clustering technique on large load data to greatly reduce calculations in reliability evaluation of restructured power systems. The method involves: first grouping a large load data into few clusters, secondly calculating partial membership value of each load point in each cluster, thirdly calculating reliability indices for each cluster and finally, expressing...
This paper addresses a novel issue of intuitionistic fuzzy c means color clustering using intuitionistic fuzzy set theory. The intuitionistic fuzzy set theory takes into the membership degree and non membership degree. Non membership degree is calculated from Sugeno type intuitionistic fuzzy complement. The introduction of another uncertainty term i.e. the non membership degree helps to converge the...
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