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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...
Community structure has been recognized as an important statistical feature of network systems over the past decade. The web service in SOA system naturally forms into some service community during execution process, within which the links between nodes are very dense, but between which they are quite sparse. These service communities were generated by repeatedly interaction between composite services...
A collaborative emergency call taking information system in the Czech Republic processes calls from the European 112 emergency number. Large amounts of various incident records are stored in its databases. The data can be used for mining spatial and temporal anomalies. When such an anomalous situation is detected so that the system could suffer from local or temporal performance decrease, either a...
The land use or land cover map depicts the physical coverage of the Earth's terrestrial surface according to its use (viz. vegetation, habitation, water body, bare soil, artificial structures etc.). Land use map generation from remotely sensed images is one of the challenging task of remote sensing technology. In this article, motivated from group forming behaviour of real ants, we have proposed two...
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...
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...
According to the characteristics of the "3-D" structure of contemporary DRAM chips, the row first column ordered (RFCO) algorithm is proposed in this paper to minimize memory access schedule length. In memory systems with a single memory controller, assuming that the memory access trace is known before scheduling, the RFCO algorithm can generate schedules which are 7.89% shorter than burst...
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...
Mashup tools are becoming increasingly important enabling users to compose services and processes on the Web. Most existing tools focus on Web-based interfaces, usability, and visual languages for creating mashups. A major challenge that has received limited attention is context-awareness and adaptivity of service mashups. In this paper we focus on two main aspects: First, a service capability model...
Clustering is used commonly in ad hoc networks for hierarchical routing. Size-bounded clustering has been proposed to restrict the maximum size of a cluster so that the routing load, and hence the energy drain, on the clusterhead is bounded. However, all the existing size-bounded clustering algorithms take the size bound as the number of nodes in the cluster. This approach may still result in high...
This paper proposes a simple technique for automatic detection and delineation of cardiac beat (QRS-complex) in Electrocardiogram (ECG) using Fuzzy C-Means (FCM) clustering algorithm. The power line interference and baseline wander present in the ECG signal is removed using digital filtering techniques. Absolute slope of the filtered ECG signal is calculated to enhance the QRS-complexes in the ECG...
Medical image fusion has been used to derive the useful information from multi modal medical images. The proposed methodology introduces evolutionary approaches for robust and automatic extraction of information from different modality images. This evolutionary fusion strategy implements multiresolution decomposition of the input images using wavelet transform. It is because, the analysis of input...
Color selection in designing user interfaces is addressed by an interactive genetic algorithm. The proposed approach is aimed at finding the optimal trade-off between different and sometimes conflicting constraints, without any explicit model of user preferences and abilities. Experimentation investigates the algorithm convergence under several conditions and user behavior.
Designing an OCR system for Indian languages in general is more complex than those of European languages due the linguistic complexity. Efforts are on the way for the development of efficient OCR systems for Indian languages, especially for Telugu, a popular South Indian language. In this paper, we proposed a method for reliable extraction of text line, word and character from document images of Telugu...
In this study, we discuss recent advances in the theory and practice of exemplar-based clustering. In the context of clustering, exemplars are those representative objects in the data sets. A recently proposed approach called convex clustering with exemplar-based models, referred as (CCE), adopts a convex objective function with a global solution. Although the existing frame work of CCE is attractive,...
An improved clustering method used for cascaded intrusion detection is proposed in this paper. It can detect different kinds of intrusions by arranging the processing framework in a cascaded way, based on which we can abstract corresponding features to achieve clustering. Computer simulations based on the 1999 KDD CUP dataset show the effectiveness of the proposed approach in detecting various intrusions...
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