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Advanced Fuzzy C-Means Algorithm Based on Local Density and Distance

Journal of Shanghai Jiaotong university(Science) 2018年第5期23卷 636-642页

作者： Shaochun PANG Yijie SHAO Sen JIANG Keyuan School of Computer Engineering and Science Shanghai UniversityShanghai 200444China Department of Computer Information Technology ＆ Graphics Purdue University NorthwestHammond 46323USA

This paper presents an advanced fuzzy C-means(FCM) clustering algorithm to overcome the weakness of the traditional FCM algorithm, including the instability of random selecting of initial center and the limitation of the data separation or the size of clusters. The advanced FCM algorithm combines the distance with density and improves the objective function so that the performance of the algorithm can be improved. The experimental results show that the proposed FCM algorithm requires fewer iterations yet provides higher accuracy than the traditional FCM algorithm. The advanced algorithm is applied to the influence of stars' box-office data, and the classification accuracy of the first class stars achieves 92.625%.

关键词： objective function clustering center fuzzy C-means (FCM) clustering algorithm degree of member-ship

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Research of Web Documents clustering Based on Dynamic Concept

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Wuhan University Journal of Natural Sciences 2004年第5期9卷 547-552页

作者： WANGYun-hua CHENShi-hong SchoolofComputer.WuhanUniversity.Wuhan430072.Hubei China： NationalEngineeringResearchComerforMultimediaSol＊ware.Wuhan130072.Hubei.China

Conceptual clustering is mainly used for solving the deficiency and incompleteness of domain knowledge. Based on conceptual clustering technology and aiming at the institutional framework and characteristic of Web theme information, this paper proposes and implements dynamic conceptual clustering algorithm and merging algorithm for Web documents, and also analyses the super performance of the clustering algorithm in efficiency and clustering accuracy. Key words conceptual clustering - clustering center - dynamic conceptual clustering - theme - web documents clustering CLC number TP 311 Foundation item: Supported by the National “863” Program of China (2002AA111010, 2003AA001032)Biography: WANG Yun-hua(1979-), male, Master candidate, research direction: knowledge engineering and data mining.

关键词： conceptual clustering clustering center dynamic conceptual clustering theme web documents clustering

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A KNN-based two-step fuzzy clustering weighted algorithm for WLAN indoor positioning

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High Technology Letters 2011年第3期17卷 223-229页

作者： Xu Yubin Sun Yongliang Ma Lin Communication Research center Harbin Institute of Technology Harbin 150080 P. R. China

Although k-nearest neighbors （KNN） is a popular fingerprint match algorithm for its simplicity and accuracy, because it is sensitive to the circumstances, a fuzzy c-means （FCM） clustering algorithm is applied to improve it. Thus, a KNN-based two-step FCM weighted （KTFW） algorithm for indoor positioning in wireless local area networks （WLAN） is presented in this paper. In KTFW algorithm, k reference points （RPs） chosen by KNN are clustered through FCM based on received signal strength （RSS） and location coordinates. The right clusters are chosen according to rules, so three sets of RPs are formed including the set of k RPs chosen by KNN and are given different weights. RPs supposed to have better contribution to positioning accuracy are given larger weights to improve the positioning accuracy. Simulation results indicate that KTFW generally outperforms KNN and its complexity is greatly reduced through providing initial clustering centers for FCM.

关键词： wireless local area networks (WLAN) indoor positioning k-nearest neighbors (KNN) fuzzy c-means (FCM) clustering center

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Research of Web Documents clustering Based on Dynamic Concept

Research of Web Documents Clustering Based on Dynamic Concep...

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The First Conference on Web Information System and Applications

作者： WANG Yun-hua, CHEN Shi-hongSchool of Computer. Wuhan University, Wuhan 130072. Huhci, China National Kngineering Research center for Multimedia Software, Wuhan 130072. Hubei, China

＜正＞ Conceptual clustering is mainly used for solving the deficiency and incompleteness of domain knowledge. Based on conceptual clustering technology and aiming at the institutional framework and characteristic of Web theme information, this paper proposes and implements dynamic conceptual clustering algorithm and merging algorithm for Web documents, and also analyses the super performance of the clustering algorithm in efficiency and clustering accuracy.

关键词： conceptual clustering clustering center dynamic conceptual clustering theme web documents clustering

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