Unsupervised Ranking and Characterization of Differentiated Clusters

被引：0

作者：

Cazzanti, Luca ^{[1
]}

Mehanian, Courosh ^{[1
]}

Penzotti, Julie ^{[1
]}

Scott, Doug ^{[1
]}

Downs, Oliver ^{[1
]}

机构：

[1] Globys Inc, Contextual Mkt Team, Seattle, WA 98119 USA

来源：

2013 IEEE INTERNATIONAL CONFERENCE ON INTELLIGENCE AND SECURITY INFORMATICS: BIG DATA, EMERGENT THREATS, AND DECISION-MAKING IN SECURITY INFORMATICS | 2013年

关键词：

clustering; dissimilarity; KL divergence; map-reduce;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

We describe a framework for automatically identifying and visualizing the most differentiating attributes of each cluster in a clustered data set. A dissimilarity function measures the cluster-conditional distinguishing saliency of each attribute with respect to a reference realization of the same attribute. For each cluster, the N attributes that are most dissimilar are presented first to the human expert, along with the overall dissimilarity of the cluster. We discuss the computational benefits of the proposed framework, how it can be implemented with map-reduce, its application to the behavioral analysis of mobile phone users, and it broad applicability to diverse problem domains.

引用

页码：266 / 266

页数：1

共 24 条

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