Towards An Intuitionistic Fuzzy Agglomerative Hierarchical Clustering Algorithm for Music Recommendation in Folksonomy

被引：6

作者：

Guan, Chun ^{[1
]}

Yuen, Kevin Kam Fung ^{[2
]}

Coenen, Frans ^{[1
]}

机构：

[1] Univ Liverpool, Dept Comp Sci, Liverpool, Merseyside, England

[2] Xian Jiaotong Liverpool Univ, Dept Comp Sci & Software Engn, Suzhou, Peoples R China

来源：

2015 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC 2015): BIG DATA ANALYTICS FOR HUMAN-CENTRIC SYSTEMS | 2015年

关键词：

Agglomerative Hierarchical Clustering; Intuitionistic Fuzzy Set; Folksonomy; Music Recommendation;

D O I：

10.1109/SMC.2015.356

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Folksonomy, a system for social tagging or collaborative tagging, is popular in Semantic Web research. Folksonomy is applied to items, such as music pieces, which their personalized tags can be annotated by users. Recommendation systems can use these tags to produce meaningful information. Clustering methods, such as the Agglomerative Hierarchical Clustering (AHC) method, can be applied in the context of recommendation system. This paper proposes the Intuitionistic Fuzzy Agglomerative Hierarchical Clustering (IFAHC) algorithm for recommendation using social tagging. The Intuitionistic Fuzzy Set (IFS) concept is used to represent tag values which are vague and uncertain. IFAHC can cluster items represented by using IFS into different groups. The application of IFAHC to music recommendation is used to demonstrate the usability of the proposed method.

引用

页码：2039 / 2042

页数：4

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