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A Targeted Retraining Scheme of Unsupervised Word Embeddings for Specific Supervised Tasks Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Query-oriented Graph Clustering

Authors : Li-Yen Kuo, Chung-Kuang Chou, Ming-Syan Chen

Published in: Advances in Knowledge Discovery and Data Mining

Publisher: Springer International Publishing

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Abstract

There are many tasks including diversified ranking and social circle discovery focusing on the relationship between data as well as the relevance to the query. These applications are actually related to query-oriented clustering. In this paper, we firstly formulate the problem, query-oriented clustering, in a general form and propose the two measures, query-oriented normalized cut (QNCut) and cluster balance to evaluate the results for query-oriented clustering. We develop a model, query-oriented graph clustering (QGC), that combines QNCut and the balance constraint based on cluster balance in a quadratic form. In the experiments, we show that QGC achieves promising results on improvement in query-oriented clustering and social circle discovery.

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Footnotes
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Metadata
Title
Query-oriented Graph Clustering
Authors
Li-Yen Kuo
Chung-Kuang Chou
Ming-Syan Chen
Copyright Year
2017
Book
Advances in Knowledge Discovery and Data Mining

Print ISBN: 978-3-319-57528-5

Electronic ISBN: 978-3-319-57529-2

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-57529-2_58

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