A new method for fuzzy information retrieval based on fuzzy hierarchical clustering and fuzzy inference techniques

Horng, Yih-Jen; Chen, Shyi‐Ming; Chang, Yu‐Chuan; Lee, Chia-Hoang

doi:10.1109/tfuzz.2004.840134

Cited by 89 publications

(8 citation statements)

References 22 publications

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“…Some VSM applications for queries, including Face Recognition [11] or Term Weighting [12] are categorized in Table 1. On the other hand, clustering is used in other works [13][14][15][16][17]. Many of the existing methods for document clustering are based on the VSM classical model, which represents every document as a vector of key terms or key phrases of a fixed size.…”

Section: Vsm-based Applicationsmentioning

confidence: 99%

“…Moreover, fuzzy semantic concepts have been used widely [15,16]. In Horng et al [15], an agglomerative hierarchical clustering method is introduced.…”

Section: Vsm-based Applicationsmentioning

confidence: 99%

“…Moreover, fuzzy semantic concepts have been used widely [15,16]. In Horng et al [15], an agglomerative hierarchical clustering method is introduced. This method overcomes the main disadvantage of the classical method, where a document cannot belong to several clusters at the same time.…”

Section: Vsm-based Applicationsmentioning

confidence: 99%

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Fuzzy Logic Applications for Knowledge Discovery: a Survey

Ropero

León

Carrasco

et al. 2011

IJACT

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In this paper, we present a survey on Fuzzy Logic (FL) applications for Knowledge Discovery (KD), focusing on Information Retrieval (IR) and Information Extraction (IE). KD has been widely used for the search of information in vague, imprecise and noisy environments. Computational Intelligence, and mainly Fuzzy Logic, emerges as an ideal tool for IR and IE systems. We introduce a huge variety of FL applications, using the two main existing approaches: the applications based on the Vector Space Model (VSM); and the applications based on ontologies. For VSM applications, we split the applications into those related to queries, clustering, user profiles and hierarchical relations. Meanwhile, we also consider ontology applications, later focusing in the semantic web. However, all these applications are closely related.Section 3 introduces the state of the art of FL applications for IE and IR. It is divided in two sections, which correspond to the two main trends nowadays: VSM (Vector Space Model)-based applications and ontology-based applications.Section 4 shows the main conclusions of this survey.

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Section: Vsm-based Applicationsmentioning

confidence: 99%

“…Moreover, fuzzy semantic concepts have been used widely [15,16]. In Horng et al [15], an agglomerative hierarchical clustering method is introduced.…”

Section: Vsm-based Applicationsmentioning

confidence: 99%

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Fuzzy Logic Applications for Knowledge Discovery: a Survey

Ropero

León

Carrasco

et al. 2011

IJACT

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“…In the clustering-based segmentation method, the point cloud segmentation is regarded as the classification process of data points with certain characteristic parameters. In the literature [9][10][11], mean-shift clustering, spectral clustering, and fuzzy clustering, respectively, are adopted to realize the segmentation of point cloud models. As well as the depth data segmentation method based on the Gaussian mixture model clustering, the normal vector of 3D point cloud is clustered using the Gaussian mixture model.…”

Section: Introductionmentioning

confidence: 99%

The Segmentation Method of Target Point Cloud for Polarization-Modulated 3D Imaging

Wang

Liu

Chen

et al. 2019

Sensors

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To implement target point cloud segmentation for a polarization-modulated 3D imaging system in practical projects, an efficient segmentation concept of multi-dimensional information fusion is designed. As the electron multiplier charge coupled device (EMCCD) camera can only acquire the gray image, but has no ability for time resolution owing to the time integration mechanism, large diameter electro-optic modulators (EOM) are used to provide time resolution for the EMCCD cameras to obtain the polarization-modulated images, from which a 3D image can also be simultaneously reconstructed. According to the characteristics of the EMCCD camera's plane array imaging, the point-to-point mapping relationship between the gray image pixels and point cloud data coordinates is established. The target's pixel coordinate position obtained by image segmentation is mapped to 3D point cloud data to get the segmented target point cloud data. On the basis of the specific environment characteristics of the experiment, the maximum entropy test method is applied to the target segmentation of the gray image, and the image morphological erosion algorithm is used to improve the segmentation accuracy. This method solves the problem that conventional point clouds' segmentation methods cannot effectively segment irregular objects or closely bound objects. Meanwhile, it has strong robustness and stability in the presence of noise, and reduces the computational complexity and data volume. The experimental results show that this method is better for the segmentation of the target in the actual environment, and can avoid the over-segmentation and under-segmentation to some extent. This paper illustrates the potential and feasibility of the segmentation method based on this system in real-time data processing.

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“…Areas of application of fuzzy cluster analysis include data analysis [1,2], information retrieval [3,4], image segmentation [5], and robotics [6]. One of the most widely used fuzzy clustering algorithm is the fuzzy C-means (FCM) algorithm [7].…”

Section: Introductionmentioning

confidence: 99%

An Overview of Unsupervised and Semi-Supervised Fuzzy Kernel Clustering

Frigui

Bchir²,

Baili³

2013

International Journal of Fuzzy Logic and Intelligent Systems

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For real-world clustering tasks, the input data is typically not easily separable due to the highly complex data structure or when clusters vary in size, density and shape. Kernel-based clustering has proven to be an effective approach to partition such data. In this paper, we provide an overview of several fuzzy kernel clustering algorithms. We focus on methods that optimize an fuzzy C-mean-type objective function. We highlight the advantages and disadvantages of each method. In addition to the completely unsupervised algorithms, we also provide an overview of some semi-supervised fuzzy kernel clustering algorithms. These algorithms use partial supervision information to guide the optimization process and avoid local minima. We also provide an overview of the different approaches that have been used to extend kernel clustering to handle very large data sets.

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A new method for fuzzy information retrieval based on fuzzy hierarchical clustering and fuzzy inference techniques

Cited by 89 publications

References 22 publications

Fuzzy Logic Applications for Knowledge Discovery: a Survey

Fuzzy Logic Applications for Knowledge Discovery: a Survey

The Segmentation Method of Target Point Cloud for Polarization-Modulated 3D Imaging

An Overview of Unsupervised and Semi-Supervised Fuzzy Kernel Clustering

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