User Oriented Semi-Supervised Document Clustering

Rong Fu Zhou; Luan Yang; Li Hua Wang; Quan Jiang Sun

doi:10.4028/www.scientific.net/AMM.644-650.1523

Paper Titles

A Layer Hybrid Intelligent Algorithm for Solving Resources Scheduling Problem
p.1506

An Particle Swarm Optimization to Alloy Tensile Organization Evolution
p.1510

Optimization Research in Heat-Moisture Comfort Evaluation System Parameters of Fabric Based on Matlab
p.1514

Determination Analysis Model of Inter-Regional Power Replacement in our Country
p.1519

User Oriented Semi-Supervised Document Clustering
p.1523

Radio Resource Allocation Algorithm Based on Bargaining Game Theory for LTE System
p.1527

Numerical Analysis of Temperature Field on the Desktop Computer Motherboard
p.1531

Structural Coupled Imaging of Multi-Physical Data Using Damped Least Squares Technique
p.1535

Enhanced Discrete Element Method (EDEM) and its Application to Stability Analysis of the Slope with Non-Through Joints
p.1539

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 644-650User Oriented Semi-Supervised Document Clustering

User Oriented Semi-Supervised Document Clustering

Abstract:

In many text mining applications, it is needed to cluster documents according to demand of users. However, Traditional documents clustering that use unsupervised learning are not able to meet this demand. In this paper, a new clustering approach that focuses on the problem is proposed. Main contributions include: (1) Expresses user requirement by topic with multiple attributes (2) Annotates topic semantic by ontology, calculate dissimilarity between topic semantics and build dissimilarity matrix. Experiments show that new approach is effective.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 644-650)

Pages:

1523-1526

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.644-650.1523

Citation:

Cite this paper

Online since:

September 2014

Authors:

Rong Fu Zhou*, Luan Yang, Li Hua Wang, Quan Jiang Sun

Keywords:

Document Clustering, Hierarchy Clustering, HowNet, Ontology, Topic Annotation

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] Hotho A, Maedche A, Staab S. Ontology-Based text document clustering[C]. In proceeding of the Conf. on Intelligent Information Systems. Zakopane: Springer-Verlag, (2003).

Google Scholar

[2] Ying Zhao, George Karypis. Topic-driven Clustering for Document Dataset[C]. In proceeding of SIAM Data Mining Conference, (2005).

DOI: 10.1137/1.9781611972757.32

Google Scholar

[3] K. Wagstaff, C. Cardie, S. Rogers and S. Schroedl. Constrained k-means clustering with background knowledge[C]. In proceeding of ICML, (2001).

Google Scholar

[4] Sugato Basu, Mikhail Bilenko and Raymond J. Monney. A probabilistic framework for semi-supervised clustering[C]. In proceeding of the 10th Int'l Conference on Knowledge Discovery and Data Mining, (2004).

DOI: 10.1145/1014052.1014062

Google Scholar

[5] Benjamin C.M. Fung, Ke Wang and Martin Ester. Hierarchical Document Clustering Using Frequent Itemsets[C]. In proceeding of the SIAM conference on Data Mining 2003 (SIAM'03).

DOI: 10.1137/1.9781611972733.6

Google Scholar

[6] Chihli Huang, Stefan Wermter and Peter Smith. Hybrid Neural Document Clustering Using Guided Self-Organization and WordNet[J]. IEEE Intelligent Systems, (2004).

DOI: 10.1109/mis.2004.1274914

Google Scholar

[7] Jian Hu, Lujun Fang, Yang Cao, Hua-Jun Zeng, Hua Li, Qiang Yang, Zheng Chen, Enhancing text clustering by leveraging Wikipedia semantics, In the Proceedings of SIGIR (2008).

DOI: 10.1145/1390334.1390367

Google Scholar