Spectral clustering has recently become one of the most popular modern clustering algorithms for traditional data. However, the application of this clustering method on geostatistical data produces spatially scattered...
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Considering the complementarity between the classification and the clustering algorithms, we propose a new feature selection method based on fuzzy Interactive Self-Organizing Data Algorithm (ISODATA). A formula for co...
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Spatial clustering is an important research topic in spatial data mining. This paper proposes algorithm SPCTR-GML for clustering spatial polygon objects based on topological relations for GML data. In the algorithm, w...
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The Mapper algorithm, a technique within topological data analysis (TDA), constructs a simplified graphical representation of high-dimensional data to uncover its underlying shape and structural patterns. The algorith...
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This paper describes a novel clustering algorithm inspired by the humoral-mediated response triggered by the adaptive immune system. The key humoral-mediated features of the algorithm include B-cell antibodies produce...
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Unlike current clustering methods, the presented vote-based clustering (VC) algorithm uses not only node location and ID information, but also battery time information. In VC, each mobile host (MH) counts Hello messag...
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ISBN:
(纸本)3540230343
Unlike current clustering methods, the presented vote-based clustering (VC) algorithm uses not only node location and ID information, but also battery time information. In VC, each mobile host (MH) counts Hello messages from its neighbors. At the same time it calculates its own vote that is the weighted sum of the normalized number of valid neighbors and its normalized remaining battery time. The one with higher vote than its neighbors will be selected preferentially as a cluster head (CH). When the number of dominated MHs of a CH is more than a balance threshold, neither of new coming MHs will be permitted to participate in the current cluster. Analysis and simulation results show that VC method can improve cluster structure than Lowest ID (LID) algorithm and Highest Degree (HD) algorithm.(1)
Density-based clustering is the task of discovering high-density regions of entities (clusters) that are separated from each other by contiguous regions of low-density. DBSCAN is, arguably, the most popular density-ba...
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In this paper, we focus on the problem of unsupervised clustering which allows automatic setting of optimal clusters number. We present a generalisation of the competitive agglomeration clustering algorithm firstly in...
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ISBN:
(纸本)0769507514
In this paper, we focus on the problem of unsupervised clustering which allows automatic setting of optimal clusters number. We present a generalisation of the competitive agglomeration clustering algorithm firstly introduced in [1]. This generalization is inspired by the regularization theory and suggests a new schema for using various cluster validity criteria continuously proposed in the literature. As a consequence of this generalization, we introduce new objective clustering functions, and present their associated optimal solutions. We present an application of this competitive clustering schema to color image segmentation in order to perform partial queries in the context of image retrieval by content. In this case, each pixel is represented by the color distribution in its vicinity. clustering algorithm has to incorporate an appropriate distance measure to compare feature vectors similarity.
This study proposes a novel method to group and organize search results. We apply statistical techniques to term co-occurrence information in a corpus to retrieve bi-grams firstly, and then combine bi-grams into n-gra...
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The problem of adapting JPEG images to satisfy constraints such as file size and resolution arises in a number of applications, from universal media access to multimedia messaging services. Visually optimized adaptati...
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