نتایج جستجو برای: dynamic clustering

تعداد نتایج: 503914  

2011
R. Pandi Selvam

Ad hoc networks are wireless, infrastructure less, multi-hop, dynamic network established by a collection of mobile nodes which provides significant features to the modern communication technologies and services. In ad-hoc networks, clustering is an important and familiar technique to divide the large network into several sub networks. According to the dynamic topology the clustering is conside...

2012
Arnaud Sallaberry Chris Muelder Kwan-Liu Ma

In this paper, we present a new approach to exploring dynamic graphs. We have developed a new clustering algorithm for dynamic graphs which finds an ideal clustering for each time-step and links the clusters together. The resulting time-varying clusters are then used to define two visual representations. The first view is an overview that shows how clusters evolve over time and provides an inte...

Journal: :Image Vision Comput. 2008
Subramanian Ramanathan Ashraf A. Kassim Tiow Seng Tan

3D dynamic meshes are associated with voluminous data and need to be encoded for efficient storage and transmission. We study the impact of vertex clustering on registration-based dynamic mesh coding, where compact mesh motion representation is achieved by computing correspondences for the mesh segments from the temporal reference to obtain high compression performance. Clustering algorithms se...

Journal: :Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing 2000
C Zhang S H Kim

Lateral clustering has emerged as a general mechanism used by many cellular receptors to control their responses to critical changes in the external environment. Here we derive a general mathematical framework to characterize the effect of receptor clustering on the sensitivity and dynamic range of biochemical signaling. In particular, we apply the theory to the bacterial chemosensory system an...

2006
Andrey Gavrilov Sungyoung Lee

An approach for invariant clustering and recognition of objects (situation) in dynamic environment is proposed. This approach is based on the combination of clustering by using unsupervised neural network (in particular ART-2) and preprocessing of sensor information by using forward multilayer perceptron (MLP) with error back propagation (EBP) which supervised by clustering neural network. Usin...

Journal: :Bioinformatics 2012
Zejun Zheng Stefan Kramer Bertil Schmidt

UNLABELLED Pyrosequencing technologies are frequently used for sequencing the 16S ribosomal RNA marker gene for profiling microbial communities. Clustering of the produced reads is an important but time-consuming task. We present Dynamic Seed-based Clustering (DySC), a new tool based on the greedy clustering approach that uses a dynamic seeding strategy. Evaluations based on the normalized mutu...

2015
Pratap Shinde

Clustering domain is vital part of data mining domain and widely used in different applications. In this project we are focusing on affinity propagation (AP) clustering which is presented recently to overcome many clustering problems in different clustering applications. Many clustering applications are based on static data. AP clustering approach is supporting only static data applications, he...

2006
Miroslav Burša Lenka Lhotská

This paper presents an overview of methods inspired by the behaviour of real ants in the nature which are currently in the focus of research and which have been used in the process of electrocardiogram interpretation and processing. The paper describes the use of dynamic time warping algorithm used together with ant colony inspired clustering and considers the relevant steps to speed up the alg...

2006
Jianying Hu Bonnie Ray Lanshan Han

We introduce an approach for model-based sequence clustering that addresses several drawbacks of existing algorithms. The approach uses a combination of Hidden Markov Modeling (HMM) for sequence estimation and Dynamic Time Warping (DTW) for hierarchical clustering, with interlocking steps of model selection, estimation and sequence grouping. We demonstrate experimentally that the algorithm can ...

2002
Ismo Kärkkäinen Pasi Fränti

Dynamic clustering problems can be solved by finding several clustering solutions with different number of clusters, and by choosing the one that minimizes a given evaluation function value. This kind of brute force approach is general but not very efficient. We propose a dynamic local search that solves the number and location of the clusters jointly. The algorithm uses a set of basic operatio...

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