Discovering Latent Semantics in Web Documents Using Fuzzy Clustering

Chiang, I-Jen; Liu, Charles Chih-Ho; Tsai, Yi-Hsin; Kumar, Ajit

doi:10.1109/tfuzz.2015.2403878

Cited by 37 publications

(7 citation statements)

References 39 publications

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“…Efficient and effective clustering methods to discover latent and coherent meanings in context are necessary. IJen Chiang et al [5] presents a fuzzy linguistic topological space along with a fuzzy clustering algorithm to discover the contextual meaning in the web documents. The proposed algorithm extracts features from the web documents using conditional random field methods and builds a fuzzy linguistic topological space based on the associations of features.…”

Section: Literature Surveymentioning

confidence: 99%

An Improved Hierarchical Clustering Using Fuzzy C-Means Clustering Technique for Document Content Analysis

Pandit¹,

Rathore²

2017

IJARCSSE

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Clustering is an unsupervised approach of data analysis. Therefore that is implemented on such environment where not some predefined patterns are available to make training. Additionally it is also required to compute the algorithm relationships among the data objects more efficiently. Therefore a rich number of applications are developed using the clustering techniques. In this presented work a clustering technique is proposed using fuzzy cmeans clustering algorithm for recognizing the text pattern from the huge data base. The proposed work is also committed to advance the approach of clustering for computing the hierarchical relationship among different data objects.

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Section: Literature Surveymentioning

confidence: 99%

An Improved Hierarchical Clustering Using Fuzzy C-Means Clustering Technique for Document Content Analysis

Pandit¹,

Rathore²

2017

IJARCSSE

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“…An agglomerative clustering approach in combination with upper similarity approximation is used for mining click stream data. Zdzislaw Pawlak introduced Rough set theory to deal with fuzziness, that is with uncertain and vague data [8]. The building block of rough set theory is an assumption that with every set of universe of discourse, some information is associated in the form of data and knowledge.…”

Section: Background Of Studymentioning

confidence: 99%

An Efficient Agglomerative Clustering Algorithm for Web Navigation Pattern Identification

Anitha¹

2016

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Web log mining is analysis of web log files with web page sequences. Discovering user access patterns from web access are necessary for building adaptive web servers, to improve e-commerce, to carry out cross-marketing, for web personalization, to predict web access sequence etc. In this paper, a new agglomerative clustering technique is proposed to identify users with similar interest, and to determine the motivation for visiting a website. Using this approach, web usage mining is done through different stages namely data cleaning, preprocessing, pattern discovery and pattern analysis. Results are given to explain how this approach produces tight usage clusters than the existing web usage mining techniques. Rather than traditional distance based clustering, the similarity measure is considered during clustering process in order to reduce computational complexity. This paper also deals with the problem of assessing the quality of user session clusters and cluster validity is measured by using statistical test, which measures the distances of clusters distributions to infer their dissimilarity and distinguish level. Using such statistical measures, it is proved that cluster accuracy is improved to the extent of 0.83, over existing k-means clustering with validity measure 0.26, FCM (Fuzzy C Means) clustering with validity measure 0.56. Rough set based clustering with validity measure 0.54 Generation of dense clusters is essential for finding interesting patterns needed for further mining and analysis.

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“…Of which, C the number of clusters is usually predefined or set by some criteria of validity or prior knowledge. So far, the fuzzy clustering has been widely studied and applied into various fields [39][40][41][42][43]. The FCM algorithm introduces the fuzziness into the attributes of each sample and each sample is assigned a membership degree between zero and one.…”

Section: The Introduction Of Fuzzy Clusteringmentioning

confidence: 99%

Kernel-Based Robust Bias-Correction Fuzzy Weighted C-Ordered-Means Clustering Algorithm

et al. 2019

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The spatial constrained Fuzzy C-means clustering (FCM) is an effective algorithm for image segmentation. Its background information improves the insensitivity to noise to some extent. In addition, the membership degree of Euclidean distance is not suitable for revealing the non-Euclidean structure of input data, since it still lacks enough robustness to noise and outliers. In order to overcome the problem above, this paper proposes a new kernel-based algorithm based on the Kernel-induced Distance Measure, which we call it Kernel-based Robust Bias-correction Fuzzy Weighted C-ordered-means Clustering Algorithm (KBFWCM). In the construction of the objective function, KBFWCM algorithm comprehensively takes into account that the spatial constrained FCM clustering algorithm is insensitive to image noise and involves a highly intensive computation. Aiming at the insensitivity of spatial constrained FCM clustering algorithm to noise and its image detail processing, the KBFWCM algorithm proposes a comprehensive algorithm combining fuzzy local similarity measures (space and grayscale) and the typicality of data attributes. Aiming at the poor robustness of the original algorithm to noise and outliers and its highly intensive computation, a Kernel-based clustering method that includes a class of robust non-Euclidean distance measures is proposed in this paper. The experimental results show that the KBFWCM algorithm has a stronger denoising and robust effect on noise image.

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Discovering Latent Semantics in Web Documents Using Fuzzy Clustering

Cited by 37 publications

References 39 publications

An Improved Hierarchical Clustering Using Fuzzy C-Means Clustering Technique for Document Content Analysis

An Improved Hierarchical Clustering Using Fuzzy C-Means Clustering Technique for Document Content Analysis

An Efficient Agglomerative Clustering Algorithm for Web Navigation Pattern Identification

Kernel-Based Robust Bias-Correction Fuzzy Weighted C-Ordered-Means Clustering Algorithm

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