Clustering with Quantitative User Preferences on Attributes

被引：0

作者：

El Moussawi, Adnan ^{[1
,2
]}

Cheriat, Ahmed ^{[1
]}

Giacomettit, Arnaud ^{[2
]}

Labroche, Nicolas ^{[2
]}

Soulet, Arnaud ^{[2
]}

机构：

[1] Kalidea Grp, Boulogne, France

[2] Univ Tours, LI, Blois, France

来源：

2016 IEEE 28TH INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE (ICTAI 2016) | 2016年

关键词：

User preference; clustering; metric learning;

D O I：

10.1109/ICTAI.2016.62

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper proposes a new semi-supervised clustering framework to represent and integrate quantitative preferences on attributes. A new metric learning algorithm is derived that achieves a compromise clustering between a data-driven and a user-driven solution and converges with a good complexity. We observe experimentally that the addition of preferences may be essential to achieve a better clustering. We also show that our approach performs better than the state-of-the art algorithms.

引用

页码：383 / 387

页数：5

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