In the realm of cognitive agents, including both human users and AI systems, explainable clustering algorithms have gained prominence. These algorithms offer enhanced transparency, making clustering results comprehens...
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When clustering a dataset, the right number k of clusters is not often obvious. And choosing k automatically is a complex problem. This paper first reviews existing methods for selecting the number of clusters for the...
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This paper proposes two new incremental fuzzy c medoids clustering algorithms for very large datasets. These algorithms are tailored to work with continuous data streams, where all the data is not necessarily availabl...
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Evaluation of software clustering algorithms is typically done by comparing the clustering results to an authoritative decomposition prepared manually by a system expert. A well-known drawback of this approach is the ...
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Basic aim of our study is to analyze the medical image. In computer vision, segmentationRefers to the process of partitioning a digital image into multiple regions. The goal ofSegmentation is to simplify and/or change...
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In this paper, the accuracies of four meta-clustering algorithms and five different base-clustering algorithms are compared. These algorithms come from different knowledge areas such as statistics, neural networks and...
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Field Programmable Gate Arrays (FPGAs) have become a popular medium for the implementation of many digital circuits. Mapping applications into FPGAs requires a set of efficient Computer-Aided Design (CAD) tools to obt...
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Data analytics and its evaluation have now become a popular practice in recent decades, with applications in a variety of fields. Data analysis is a computational statistical technique which includes a wide variety of...
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Image segmentation deals with partitioning an input image into disjoint/non-overlapping regions. Among different segmentation algorithms, level set methods have been very popular. Less sensitivity to initialization, a...
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ISBN:
(纸本)9781424455614
Image segmentation deals with partitioning an input image into disjoint/non-overlapping regions. Among different segmentation algorithms, level set methods have been very popular. Less sensitivity to initialization, ability to split and merge the contour, and also, involving statistical inference have made level set even more accepted than similar methods like snakes. However, it is very time-consuming. To solve this problem, in this paper a fast variational approach is presented for texture segmentation. For this purpose, first a feature space based on non-linear diffusion is set up from CIE L*a*b* colour components. Then, this feature space is clustered by fusion of clustering algorithms. Finally, the produced cluster map is used in level set for contour evolution. As it is shown in the simulation results, our algorithm is robust in segmenting noisy texture. Also, it is faster than previous level set approaches for texture segmentation.
A single linkage clustering algorithm adapted for points distributed over time (SLOT) is used to recover clusters from coplanar points associated with time parameters. Cluster number and shape determine separability a...
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ISBN:
(纸本)0818608455
A single linkage clustering algorithm adapted for points distributed over time (SLOT) is used to recover clusters from coplanar points associated with time parameters. Cluster number and shape determine separability and hence effectiveness of the algorithm. Performance in simulation experiments also depended on the probability of recording cluster points. SLOT links points observed at different times if they are within some limiting distance/ and the distance parameter becomes critical when detect-ability is low. Performance comparisons are made with other algorithms;and results are presented in the context of a rule-based expert system for solving problems involving cluster analysis of time-dependent spatial patterns.
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